958 lines
45 KiB
Python
958 lines
45 KiB
Python
# Copyright 2025 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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from dataclasses import dataclass
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from typing import Any, Callable, Dict, List, Optional, Union
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import numpy as np
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import PIL
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import torch
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from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection
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from ...image_processor import PipelineImageInput
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from ...loaders import FromSingleFileMixin, IPAdapterMixin, StableDiffusionLoraLoaderMixin, TextualInversionLoaderMixin
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from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel, UNetMotionModel
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from ...models.lora import adjust_lora_scale_text_encoder
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from ...models.unets.unet_motion_model import MotionAdapter
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from ...schedulers import (
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DDIMScheduler,
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DPMSolverMultistepScheduler,
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EulerAncestralDiscreteScheduler,
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EulerDiscreteScheduler,
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LMSDiscreteScheduler,
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PNDMScheduler,
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)
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from ...utils import (
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USE_PEFT_BACKEND,
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BaseOutput,
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is_torch_xla_available,
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logging,
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replace_example_docstring,
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scale_lora_layers,
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unscale_lora_layers,
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)
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from ...utils.torch_utils import randn_tensor
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from ...video_processor import VideoProcessor
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from ..free_init_utils import FreeInitMixin
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from ..pipeline_utils import DeprecatedPipelineMixin, DiffusionPipeline, StableDiffusionMixin
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if is_torch_xla_available():
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import torch_xla.core.xla_model as xm
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XLA_AVAILABLE = True
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else:
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XLA_AVAILABLE = False
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logger = logging.get_logger(__name__) # pylint: disable=invalid-name
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EXAMPLE_DOC_STRING = """
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Examples:
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```py
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>>> import torch
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>>> from diffusers import EulerDiscreteScheduler, MotionAdapter, PIAPipeline
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>>> from diffusers.utils import export_to_gif, load_image
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>>> adapter = MotionAdapter.from_pretrained("openmmlab/PIA-condition-adapter")
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>>> pipe = PIAPipeline.from_pretrained(
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... "SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter, torch_dtype=torch.float16
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... )
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>>> pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config)
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>>> image = load_image(
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... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true"
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... )
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>>> image = image.resize((512, 512))
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>>> prompt = "cat in a hat"
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>>> negative_prompt = "wrong white balance, dark, sketches, worst quality, low quality, deformed, distorted"
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>>> generator = torch.Generator("cpu").manual_seed(0)
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>>> output = pipe(image=image, prompt=prompt, negative_prompt=negative_prompt, generator=generator)
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>>> frames = output.frames[0]
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>>> export_to_gif(frames, "pia-animation.gif")
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```
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"""
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RANGE_LIST = [
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[1.0, 0.9, 0.85, 0.85, 0.85, 0.8], # 0 Small Motion
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[1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75], # Moderate Motion
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[1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5, 0.5], # Large Motion
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[1.0, 0.9, 0.85, 0.85, 0.85, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.85, 0.85, 0.9, 1.0], # Loop
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[1.0, 0.8, 0.8, 0.8, 0.79, 0.78, 0.75, 0.75, 0.75, 0.75, 0.75, 0.78, 0.79, 0.8, 0.8, 1.0], # Loop
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[1.0, 0.8, 0.7, 0.7, 0.7, 0.7, 0.6, 0.5, 0.5, 0.6, 0.7, 0.7, 0.7, 0.7, 0.8, 1.0], # Loop
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[0.5, 0.4, 0.4, 0.4, 0.35, 0.3], # Style Transfer Candidate Small Motion
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[0.5, 0.4, 0.4, 0.4, 0.35, 0.35, 0.3, 0.25, 0.2], # Style Transfer Moderate Motion
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[0.5, 0.2], # Style Transfer Large Motion
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]
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def prepare_mask_coef_by_statistics(num_frames: int, cond_frame: int, motion_scale: int):
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assert num_frames > 0, "video_length should be greater than 0"
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assert num_frames > cond_frame, "video_length should be greater than cond_frame"
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range_list = RANGE_LIST
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assert motion_scale < len(range_list), f"motion_scale type{motion_scale} not implemented"
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coef = range_list[motion_scale]
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coef = coef + ([coef[-1]] * (num_frames - len(coef)))
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order = [abs(i - cond_frame) for i in range(num_frames)]
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coef = [coef[order[i]] for i in range(num_frames)]
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return coef
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@dataclass
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class PIAPipelineOutput(BaseOutput):
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r"""
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Output class for PIAPipeline.
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Args:
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frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]):
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Nested list of length `batch_size` with denoised PIL image sequences of length `num_frames`, NumPy array of
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shape `(batch_size, num_frames, channels, height, width, Torch tensor of shape `(batch_size, num_frames,
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channels, height, width)`.
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"""
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frames: Union[torch.Tensor, np.ndarray, List[List[PIL.Image.Image]]]
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class PIAPipeline(
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DeprecatedPipelineMixin,
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DiffusionPipeline,
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StableDiffusionMixin,
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TextualInversionLoaderMixin,
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IPAdapterMixin,
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StableDiffusionLoraLoaderMixin,
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FromSingleFileMixin,
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FreeInitMixin,
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):
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_last_supported_version = "0.33.1"
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r"""
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Pipeline for text-to-video generation.
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This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
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implemented for all pipelines (downloading, saving, running on a particular device, etc.).
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The pipeline also inherits the following loading methods:
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- [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings
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- [`~loaders.StableDiffusionLoraLoaderMixin.load_lora_weights`] for loading LoRA weights
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- [`~loaders.StableDiffusionLoraLoaderMixin.save_lora_weights`] for saving LoRA weights
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- [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters
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Args:
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vae ([`AutoencoderKL`]):
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Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
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text_encoder ([`CLIPTextModel`]):
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Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)).
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tokenizer (`CLIPTokenizer`):
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A [`~transformers.CLIPTokenizer`] to tokenize text.
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unet ([`UNet2DConditionModel`]):
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A [`UNet2DConditionModel`] used to create a UNetMotionModel to denoise the encoded video latents.
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motion_adapter ([`MotionAdapter`]):
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A [`MotionAdapter`] to be used in combination with `unet` to denoise the encoded video latents.
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scheduler ([`SchedulerMixin`]):
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A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of
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[`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
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"""
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model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae"
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_optional_components = ["feature_extractor", "image_encoder", "motion_adapter"]
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_callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"]
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def __init__(
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self,
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vae: AutoencoderKL,
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text_encoder: CLIPTextModel,
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tokenizer: CLIPTokenizer,
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unet: Union[UNet2DConditionModel, UNetMotionModel],
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scheduler: Union[
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DDIMScheduler,
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PNDMScheduler,
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LMSDiscreteScheduler,
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EulerDiscreteScheduler,
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EulerAncestralDiscreteScheduler,
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DPMSolverMultistepScheduler,
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],
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motion_adapter: Optional[MotionAdapter] = None,
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feature_extractor: CLIPImageProcessor = None,
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image_encoder: CLIPVisionModelWithProjection = None,
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):
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super().__init__()
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if isinstance(unet, UNet2DConditionModel):
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unet = UNetMotionModel.from_unet2d(unet, motion_adapter)
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self.register_modules(
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vae=vae,
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text_encoder=text_encoder,
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tokenizer=tokenizer,
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unet=unet,
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motion_adapter=motion_adapter,
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scheduler=scheduler,
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feature_extractor=feature_extractor,
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image_encoder=image_encoder,
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)
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self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) if getattr(self, "vae", None) else 8
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self.video_processor = VideoProcessor(do_resize=False, vae_scale_factor=self.vae_scale_factor)
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt with num_images_per_prompt -> num_videos_per_prompt
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def encode_prompt(
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self,
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prompt,
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device,
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num_images_per_prompt,
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do_classifier_free_guidance,
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negative_prompt=None,
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prompt_embeds: Optional[torch.Tensor] = None,
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negative_prompt_embeds: Optional[torch.Tensor] = None,
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lora_scale: Optional[float] = None,
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clip_skip: Optional[int] = None,
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):
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r"""
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Encodes the prompt into text encoder hidden states.
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Args:
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prompt (`str` or `List[str]`, *optional*):
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prompt to be encoded
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device: (`torch.device`):
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torch device
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num_images_per_prompt (`int`):
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number of images that should be generated per prompt
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do_classifier_free_guidance (`bool`):
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whether to use classifier free guidance or not
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negative_prompt (`str` or `List[str]`, *optional*):
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The prompt or prompts not to guide the image generation. If not defined, one has to pass
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`negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is
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less than `1`).
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prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
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provided, text embeddings will be generated from `prompt` input argument.
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negative_prompt_embeds (`torch.Tensor`, *optional*):
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Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
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weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
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argument.
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lora_scale (`float`, *optional*):
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A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
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clip_skip (`int`, *optional*):
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Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that
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the output of the pre-final layer will be used for computing the prompt embeddings.
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"""
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# set lora scale so that monkey patched LoRA
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# function of text encoder can correctly access it
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if lora_scale is not None and isinstance(self, StableDiffusionLoraLoaderMixin):
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self._lora_scale = lora_scale
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# dynamically adjust the LoRA scale
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if not USE_PEFT_BACKEND:
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adjust_lora_scale_text_encoder(self.text_encoder, lora_scale)
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else:
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scale_lora_layers(self.text_encoder, lora_scale)
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if prompt is not None and isinstance(prompt, str):
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batch_size = 1
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elif prompt is not None and isinstance(prompt, list):
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batch_size = len(prompt)
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else:
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batch_size = prompt_embeds.shape[0]
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if prompt_embeds is None:
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# textual inversion: process multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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prompt = self.maybe_convert_prompt(prompt, self.tokenizer)
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text_inputs = self.tokenizer(
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prompt,
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padding="max_length",
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max_length=self.tokenizer.model_max_length,
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truncation=True,
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return_tensors="pt",
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)
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text_input_ids = text_inputs.input_ids
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untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
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if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
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text_input_ids, untruncated_ids
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):
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removed_text = self.tokenizer.batch_decode(
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untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]
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)
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logger.warning(
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"The following part of your input was truncated because CLIP can only handle sequences up to"
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f" {self.tokenizer.model_max_length} tokens: {removed_text}"
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)
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = text_inputs.attention_mask.to(device)
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else:
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attention_mask = None
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if clip_skip is None:
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prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask)
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prompt_embeds = prompt_embeds[0]
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else:
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prompt_embeds = self.text_encoder(
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text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True
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)
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# Access the `hidden_states` first, that contains a tuple of
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# all the hidden states from the encoder layers. Then index into
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# the tuple to access the hidden states from the desired layer.
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prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)]
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# We also need to apply the final LayerNorm here to not mess with the
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# representations. The `last_hidden_states` that we typically use for
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# obtaining the final prompt representations passes through the LayerNorm
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# layer.
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prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds)
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if self.text_encoder is not None:
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prompt_embeds_dtype = self.text_encoder.dtype
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elif self.unet is not None:
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prompt_embeds_dtype = self.unet.dtype
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else:
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prompt_embeds_dtype = prompt_embeds.dtype
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prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
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bs_embed, seq_len, _ = prompt_embeds.shape
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# duplicate text embeddings for each generation per prompt, using mps friendly method
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prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1)
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prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1)
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# get unconditional embeddings for classifier free guidance
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if do_classifier_free_guidance and negative_prompt_embeds is None:
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uncond_tokens: List[str]
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if negative_prompt is None:
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uncond_tokens = [""] * batch_size
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elif prompt is not None and type(prompt) is not type(negative_prompt):
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raise TypeError(
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f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
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f" {type(prompt)}."
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)
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elif isinstance(negative_prompt, str):
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uncond_tokens = [negative_prompt]
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elif batch_size != len(negative_prompt):
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raise ValueError(
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f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
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f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
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" the batch size of `prompt`."
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)
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else:
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uncond_tokens = negative_prompt
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# textual inversion: process multi-vector tokens if necessary
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if isinstance(self, TextualInversionLoaderMixin):
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uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer)
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max_length = prompt_embeds.shape[1]
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uncond_input = self.tokenizer(
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uncond_tokens,
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padding="max_length",
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max_length=max_length,
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truncation=True,
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return_tensors="pt",
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)
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if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask:
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attention_mask = uncond_input.attention_mask.to(device)
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else:
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attention_mask = None
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negative_prompt_embeds = self.text_encoder(
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uncond_input.input_ids.to(device),
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attention_mask=attention_mask,
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)
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negative_prompt_embeds = negative_prompt_embeds[0]
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if do_classifier_free_guidance:
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# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
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seq_len = negative_prompt_embeds.shape[1]
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negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device)
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negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1)
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negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1)
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if self.text_encoder is not None:
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if isinstance(self, StableDiffusionLoraLoaderMixin) and USE_PEFT_BACKEND:
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# Retrieve the original scale by scaling back the LoRA layers
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unscale_lora_layers(self.text_encoder, lora_scale)
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return prompt_embeds, negative_prompt_embeds
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# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image
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def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None):
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dtype = next(self.image_encoder.parameters()).dtype
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if not isinstance(image, torch.Tensor):
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image = self.feature_extractor(image, return_tensors="pt").pixel_values
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image = image.to(device=device, dtype=dtype)
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if output_hidden_states:
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image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2]
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image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0)
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uncond_image_enc_hidden_states = self.image_encoder(
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torch.zeros_like(image), output_hidden_states=True
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).hidden_states[-2]
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uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave(
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num_images_per_prompt, dim=0
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)
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return image_enc_hidden_states, uncond_image_enc_hidden_states
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else:
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image_embeds = self.image_encoder(image).image_embeds
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image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0)
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uncond_image_embeds = torch.zeros_like(image_embeds)
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return image_embeds, uncond_image_embeds
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# Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents
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def decode_latents(self, latents):
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latents = 1 / self.vae.config.scaling_factor * latents
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|
|
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,
|
|
height,
|
|
width,
|
|
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 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) and not isinstance(prompt, list)):
|
|
raise ValueError(f"`prompt` has to be of type `str` or `list` 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 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"
|
|
)
|
|
|
|
# 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.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 prepare_masked_condition(
|
|
self,
|
|
image,
|
|
batch_size,
|
|
num_channels_latents,
|
|
num_frames,
|
|
height,
|
|
width,
|
|
dtype,
|
|
device,
|
|
generator,
|
|
motion_scale=0,
|
|
):
|
|
shape = (
|
|
batch_size,
|
|
num_channels_latents,
|
|
num_frames,
|
|
height // self.vae_scale_factor,
|
|
width // self.vae_scale_factor,
|
|
)
|
|
_, _, _, scaled_height, scaled_width = shape
|
|
|
|
image = self.video_processor.preprocess(image)
|
|
image = image.to(device, dtype)
|
|
|
|
if isinstance(generator, list):
|
|
image_latent = [
|
|
self.vae.encode(image[k : k + 1]).latent_dist.sample(generator[k]) for k in range(batch_size)
|
|
]
|
|
image_latent = torch.cat(image_latent, dim=0)
|
|
else:
|
|
image_latent = self.vae.encode(image).latent_dist.sample(generator)
|
|
|
|
image_latent = image_latent.to(device=device, dtype=dtype)
|
|
image_latent = torch.nn.functional.interpolate(image_latent, size=[scaled_height, scaled_width])
|
|
image_latent_padding = image_latent.clone() * self.vae.config.scaling_factor
|
|
|
|
mask = torch.zeros((batch_size, 1, num_frames, scaled_height, scaled_width)).to(device=device, dtype=dtype)
|
|
mask_coef = prepare_mask_coef_by_statistics(num_frames, 0, motion_scale)
|
|
masked_image = torch.zeros(batch_size, 4, num_frames, scaled_height, scaled_width).to(
|
|
device=device, dtype=self.unet.dtype
|
|
)
|
|
for f in range(num_frames):
|
|
mask[:, :, f, :, :] = mask_coef[f]
|
|
masked_image[:, :, f, :, :] = image_latent_padding.clone()
|
|
|
|
mask = torch.cat([mask] * 2) if self.do_classifier_free_guidance else mask
|
|
masked_image = torch.cat([masked_image] * 2) if self.do_classifier_free_guidance else masked_image
|
|
|
|
return mask, masked_image
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_img2img.StableDiffusionImg2ImgPipeline.get_timesteps
|
|
def get_timesteps(self, num_inference_steps, 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 = self.scheduler.timesteps[t_start * self.scheduler.order :]
|
|
if hasattr(self.scheduler, "set_begin_index"):
|
|
self.scheduler.set_begin_index(t_start * self.scheduler.order)
|
|
|
|
return timesteps, num_inference_steps - t_start
|
|
|
|
@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
|
|
|
|
@torch.no_grad()
|
|
@replace_example_docstring(EXAMPLE_DOC_STRING)
|
|
def __call__(
|
|
self,
|
|
image: PipelineImageInput,
|
|
prompt: Union[str, List[str]] = None,
|
|
strength: float = 1.0,
|
|
num_frames: Optional[int] = 16,
|
|
height: Optional[int] = None,
|
|
width: Optional[int] = None,
|
|
num_inference_steps: int = 50,
|
|
guidance_scale: float = 7.5,
|
|
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,
|
|
motion_scale: int = 0,
|
|
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"],
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
image (`PipelineImageInput`):
|
|
The input image to be used for video generation.
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
strength (`float`, *optional*, defaults to 1.0):
|
|
Indicates extent to transform the reference `image`. Must be between 0 and 1.
|
|
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_frames (`int`, *optional*, defaults to 16):
|
|
The number of video frames that are generated. Defaults to 16 frames which at 8 frames per seconds
|
|
amounts to 2 seconds of 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.
|
|
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.
|
|
motion_scale: (`int`, *optional*, defaults to 0):
|
|
Parameter that controls the amount and type of motion that is added to the image. Increasing the value
|
|
increases the amount of motion, while specific ranges of values control the type of motion that is
|
|
added. Must be between 0 and 8. Set between 0-2 to only increase the amount of motion. Set between 3-5
|
|
to create looping motion. Set between 6-8 to perform motion with image style transfer.
|
|
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 [`~pipelines.text_to_video_synthesis.TextToVideoSDPipelineOutput`] 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.
|
|
|
|
Examples:
|
|
|
|
Returns:
|
|
[`~pipelines.pia.pipeline_pia.PIAPipelineOutput`] or `tuple`:
|
|
If `return_dict` is `True`, [`~pipelines.pia.pipeline_pia.PIAPipelineOutput`] 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,
|
|
height,
|
|
width,
|
|
negative_prompt,
|
|
prompt_embeds,
|
|
negative_prompt_embeds,
|
|
ip_adapter_image,
|
|
ip_adapter_image_embeds,
|
|
callback_on_step_end_tensor_inputs,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
|
|
# 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
|
|
text_encoder_lora_scale = (
|
|
self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None
|
|
)
|
|
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)
|
|
|
|
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,
|
|
)
|
|
|
|
# 4. Prepare timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
|
|
latent_timestep = timesteps[:1].repeat(batch_size * num_videos_per_prompt)
|
|
self._num_timesteps = len(timesteps)
|
|
|
|
# 5. Prepare latent variables
|
|
latents = self.prepare_latents(
|
|
batch_size * num_videos_per_prompt,
|
|
4,
|
|
num_frames,
|
|
height,
|
|
width,
|
|
prompt_embeds.dtype,
|
|
device,
|
|
generator,
|
|
latents=latents,
|
|
)
|
|
mask, masked_image = self.prepare_masked_condition(
|
|
image,
|
|
batch_size * num_videos_per_prompt,
|
|
4,
|
|
num_frames=num_frames,
|
|
height=height,
|
|
width=width,
|
|
dtype=self.unet.dtype,
|
|
device=device,
|
|
generator=generator,
|
|
motion_scale=motion_scale,
|
|
)
|
|
if strength < 1.0:
|
|
noise = randn_tensor(latents.shape, generator=generator, device=device, dtype=latents.dtype)
|
|
latents = self.scheduler.add_noise(masked_image[0], noise, latent_timestep)
|
|
|
|
# 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. 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
|
|
)
|
|
|
|
# 8. Denoising loop
|
|
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)
|
|
num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
|
|
|
|
with self.progress_bar(total=self._num_timesteps) as progress_bar:
|
|
for i, t in enumerate(timesteps):
|
|
# expand the latents if we are doing classifier free guidance
|
|
latent_model_input = torch.cat([latents] * 2) if self.do_classifier_free_guidance else latents
|
|
latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)
|
|
latent_model_input = torch.cat([latent_model_input, mask, masked_image], dim=1)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=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()
|
|
|
|
# 9. 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)
|
|
|
|
# 10. Offload all models
|
|
self.maybe_free_model_hooks()
|
|
|
|
if not return_dict:
|
|
return (video,)
|
|
|
|
return PIAPipelineOutput(frames=video)
|