1118 lines
55 KiB
Python
1118 lines
55 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 typing import Any, Callable, Dict, List, Optional, Tuple, Union
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import torch
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import torch.nn.functional as F
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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 (
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AutoencoderKL,
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ControlNetModel,
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ImageProjection,
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MultiControlNetModel,
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UNet2DConditionModel,
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UNetMotionModel,
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)
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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 KarrasDiffusionSchedulers
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from ...utils import USE_PEFT_BACKEND, is_torch_xla_available, logging, scale_lora_layers, unscale_lora_layers
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from ...utils.torch_utils import is_compiled_module, 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 ..free_noise_utils import AnimateDiffFreeNoiseMixin
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from ..pipeline_utils import DiffusionPipeline, StableDiffusionMixin
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from .pipeline_output import AnimateDiffPipelineOutput
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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 (
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... AnimateDiffControlNetPipeline,
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... AutoencoderKL,
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... ControlNetModel,
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... MotionAdapter,
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... LCMScheduler,
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... )
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>>> from diffusers.utils import export_to_gif, load_video
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>>> # Additionally, you will need a preprocess videos before they can be used with the ControlNet
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>>> # HF maintains just the right package for it: `pip install controlnet_aux`
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>>> from controlnet_aux.processor import ZoeDetector
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>>> # Download controlnets from https://huggingface.co/lllyasviel/ControlNet-v1-1 to use .from_single_file
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>>> # Download Diffusers-format controlnets, such as https://huggingface.co/lllyasviel/sd-controlnet-depth, to use .from_pretrained()
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>>> controlnet = ControlNetModel.from_single_file("control_v11f1p_sd15_depth.pth", torch_dtype=torch.float16)
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>>> # We use AnimateLCM for this example but one can use the original motion adapters as well (for example, https://huggingface.co/guoyww/animatediff-motion-adapter-v1-5-3)
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>>> motion_adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM")
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>>> vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16)
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>>> pipe: AnimateDiffControlNetPipeline = AnimateDiffControlNetPipeline.from_pretrained(
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... "SG161222/Realistic_Vision_V5.1_noVAE",
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... motion_adapter=motion_adapter,
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... controlnet=controlnet,
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... vae=vae,
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... ).to(device="cuda", dtype=torch.float16)
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>>> pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")
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>>> pipe.load_lora_weights(
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... "wangfuyun/AnimateLCM", weight_name="AnimateLCM_sd15_t2v_lora.safetensors", adapter_name="lcm-lora"
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... )
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>>> pipe.set_adapters(["lcm-lora"], [0.8])
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>>> depth_detector = ZoeDetector.from_pretrained("lllyasviel/Annotators").to("cuda")
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>>> video = load_video(
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... "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif"
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... )
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>>> conditioning_frames = []
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>>> with pipe.progress_bar(total=len(video)) as progress_bar:
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... for frame in video:
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... conditioning_frames.append(depth_detector(frame))
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... progress_bar.update()
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>>> prompt = "a panda, playing a guitar, sitting in a pink boat, in the ocean, mountains in background, realistic, high quality"
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>>> negative_prompt = "bad quality, worst quality"
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>>> video = pipe(
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... prompt=prompt,
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... negative_prompt=negative_prompt,
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... num_frames=len(video),
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... num_inference_steps=10,
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... guidance_scale=2.0,
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... conditioning_frames=conditioning_frames,
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... generator=torch.Generator().manual_seed(42),
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... ).frames[0]
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>>> export_to_gif(video, "animatediff_controlnet.gif", fps=8)
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```
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"""
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class AnimateDiffControlNetPipeline(
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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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FreeInitMixin,
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AnimateDiffFreeNoiseMixin,
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FromSingleFileMixin,
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):
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r"""
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Pipeline for text-to-video generation with ControlNet guidance.
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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->unet->vae"
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_optional_components = ["feature_extractor", "image_encoder"]
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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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motion_adapter: MotionAdapter,
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controlnet: Union[ControlNetModel, List[ControlNetModel], Tuple[ControlNetModel], MultiControlNetModel],
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scheduler: KarrasDiffusionSchedulers,
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feature_extractor: Optional[CLIPImageProcessor] = None,
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image_encoder: Optional[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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if isinstance(controlnet, (list, tuple)):
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controlnet = MultiControlNetModel(controlnet)
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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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controlnet=controlnet,
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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(vae_scale_factor=self.vae_scale_factor)
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self.control_video_processor = VideoProcessor(
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vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True, do_normalize=False
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)
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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.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds
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def prepare_ip_adapter_image_embeds(
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self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance
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):
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image_embeds = []
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if do_classifier_free_guidance:
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negative_image_embeds = []
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if ip_adapter_image_embeds is None:
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if not isinstance(ip_adapter_image, list):
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ip_adapter_image = [ip_adapter_image]
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if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers):
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raise ValueError(
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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."
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)
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for single_ip_adapter_image, image_proj_layer in zip(
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|
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.animatediff.pipeline_animatediff.AnimateDiffPipeline.decode_latents
|
|
def decode_latents(self, latents, decode_chunk_size: int = 16):
|
|
latents = 1 / self.vae.config.scaling_factor * latents
|
|
|
|
batch_size, channels, num_frames, height, width = latents.shape
|
|
latents = latents.permute(0, 2, 1, 3, 4).reshape(batch_size * num_frames, channels, height, width)
|
|
|
|
video = []
|
|
for i in range(0, latents.shape[0], decode_chunk_size):
|
|
batch_latents = latents[i : i + decode_chunk_size]
|
|
batch_latents = self.vae.decode(batch_latents).sample
|
|
video.append(batch_latents)
|
|
|
|
video = torch.cat(video)
|
|
video = video[None, :].reshape((batch_size, num_frames, -1) + video.shape[2:]).permute(0, 2, 1, 3, 4)
|
|
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
|
|
video = video.float()
|
|
return video
|
|
|
|
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
|
|
def prepare_extra_step_kwargs(self, generator, eta):
|
|
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
|
|
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
|
|
# eta corresponds to η in DDIM paper: https://huggingface.co/papers/2010.02502
|
|
# and should be between [0, 1]
|
|
|
|
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
extra_step_kwargs = {}
|
|
if accepts_eta:
|
|
extra_step_kwargs["eta"] = eta
|
|
|
|
# check if the scheduler accepts generator
|
|
accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
|
|
if accepts_generator:
|
|
extra_step_kwargs["generator"] = generator
|
|
return extra_step_kwargs
|
|
|
|
def check_inputs(
|
|
self,
|
|
prompt,
|
|
height,
|
|
width,
|
|
num_frames,
|
|
negative_prompt=None,
|
|
prompt_embeds=None,
|
|
negative_prompt_embeds=None,
|
|
callback_on_step_end_tensor_inputs=None,
|
|
video=None,
|
|
controlnet_conditioning_scale=1.0,
|
|
control_guidance_start=0.0,
|
|
control_guidance_end=1.0,
|
|
):
|
|
if height % 8 != 0 or width % 8 != 0:
|
|
raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")
|
|
|
|
if callback_on_step_end_tensor_inputs is not None and not all(
|
|
k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs
|
|
):
|
|
raise ValueError(
|
|
f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}"
|
|
)
|
|
|
|
if prompt is not None and prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
|
|
" only forward one of the two."
|
|
)
|
|
elif prompt is None and prompt_embeds is None:
|
|
raise ValueError(
|
|
"Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
|
|
)
|
|
elif prompt is not None and not isinstance(prompt, (str, list, dict)):
|
|
raise ValueError(f"`prompt` has to be of type `str`, `list` or `dict` but is {type(prompt)}")
|
|
|
|
if negative_prompt is not None and negative_prompt_embeds is not None:
|
|
raise ValueError(
|
|
f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
|
|
f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
|
|
)
|
|
|
|
if prompt_embeds is not None and negative_prompt_embeds is not None:
|
|
if prompt_embeds.shape != negative_prompt_embeds.shape:
|
|
raise ValueError(
|
|
"`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
|
|
f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
|
|
f" {negative_prompt_embeds.shape}."
|
|
)
|
|
|
|
# `prompt` needs more sophisticated handling when there are multiple
|
|
# conditionings.
|
|
if isinstance(self.controlnet, MultiControlNetModel):
|
|
if isinstance(prompt, list):
|
|
logger.warning(
|
|
f"You have {len(self.controlnet.nets)} ControlNets and you have passed {len(prompt)}"
|
|
" prompts. The conditionings will be fixed across the prompts."
|
|
)
|
|
|
|
# Check `image`
|
|
is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance(
|
|
self.controlnet, torch._dynamo.eval_frame.OptimizedModule
|
|
)
|
|
if (
|
|
isinstance(self.controlnet, ControlNetModel)
|
|
or is_compiled
|
|
and isinstance(self.controlnet._orig_mod, ControlNetModel)
|
|
):
|
|
if not isinstance(video, list):
|
|
raise TypeError(f"For single controlnet, `image` must be of type `list` but got {type(video)}")
|
|
if len(video) != num_frames:
|
|
raise ValueError(f"Excepted image to have length {num_frames} but got {len(video)=}")
|
|
elif (
|
|
isinstance(self.controlnet, MultiControlNetModel)
|
|
or is_compiled
|
|
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
|
|
):
|
|
if not isinstance(video, list) or not isinstance(video[0], list):
|
|
raise TypeError(f"For multiple controlnets: `image` must be type list of lists but got {type(video)=}")
|
|
if len(video[0]) != num_frames:
|
|
raise ValueError(f"Expected length of image sublist as {num_frames} but got {len(video[0])=}")
|
|
if any(len(img) != len(video[0]) for img in video):
|
|
raise ValueError("All conditioning frame batches for multicontrolnet must be same size")
|
|
else:
|
|
assert False
|
|
|
|
# Check `controlnet_conditioning_scale`
|
|
if (
|
|
isinstance(self.controlnet, ControlNetModel)
|
|
or is_compiled
|
|
and isinstance(self.controlnet._orig_mod, ControlNetModel)
|
|
):
|
|
if not isinstance(controlnet_conditioning_scale, float):
|
|
raise TypeError("For single controlnet: `controlnet_conditioning_scale` must be type `float`.")
|
|
elif (
|
|
isinstance(self.controlnet, MultiControlNetModel)
|
|
or is_compiled
|
|
and isinstance(self.controlnet._orig_mod, MultiControlNetModel)
|
|
):
|
|
if isinstance(controlnet_conditioning_scale, list):
|
|
if any(isinstance(i, list) for i in controlnet_conditioning_scale):
|
|
raise ValueError("A single batch of multiple conditionings are supported at the moment.")
|
|
elif isinstance(controlnet_conditioning_scale, list) and len(controlnet_conditioning_scale) != len(
|
|
self.controlnet.nets
|
|
):
|
|
raise ValueError(
|
|
"For multiple controlnets: When `controlnet_conditioning_scale` is specified as `list`, it must have"
|
|
" the same length as the number of controlnets"
|
|
)
|
|
else:
|
|
assert False
|
|
|
|
if not isinstance(control_guidance_start, (tuple, list)):
|
|
control_guidance_start = [control_guidance_start]
|
|
|
|
if not isinstance(control_guidance_end, (tuple, list)):
|
|
control_guidance_end = [control_guidance_end]
|
|
|
|
if len(control_guidance_start) != len(control_guidance_end):
|
|
raise ValueError(
|
|
f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list."
|
|
)
|
|
|
|
if isinstance(self.controlnet, MultiControlNetModel):
|
|
if len(control_guidance_start) != len(self.controlnet.nets):
|
|
raise ValueError(
|
|
f"`control_guidance_start`: {control_guidance_start} has {len(control_guidance_start)} elements but there are {len(self.controlnet.nets)} controlnets available. Make sure to provide {len(self.controlnet.nets)}."
|
|
)
|
|
|
|
for start, end in zip(control_guidance_start, control_guidance_end):
|
|
if start >= end:
|
|
raise ValueError(
|
|
f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}."
|
|
)
|
|
if start < 0.0:
|
|
raise ValueError(f"control guidance start: {start} can't be smaller than 0.")
|
|
if end > 1.0:
|
|
raise ValueError(f"control guidance end: {end} can't be larger than 1.0.")
|
|
|
|
# Copied from diffusers.pipelines.animatediff.pipeline_animatediff.AnimateDiffPipeline.prepare_latents
|
|
def prepare_latents(
|
|
self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None
|
|
):
|
|
# If FreeNoise is enabled, generate latents as described in Equation (7) of [FreeNoise](https://huggingface.co/papers/2310.15169)
|
|
if self.free_noise_enabled:
|
|
latents = self._prepare_latents_free_noise(
|
|
batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents
|
|
)
|
|
|
|
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."
|
|
)
|
|
|
|
shape = (
|
|
batch_size,
|
|
num_channels_latents,
|
|
num_frames,
|
|
height // self.vae_scale_factor,
|
|
width // self.vae_scale_factor,
|
|
)
|
|
|
|
if latents is None:
|
|
latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype)
|
|
else:
|
|
latents = latents.to(device)
|
|
|
|
# scale the initial noise by the standard deviation required by the scheduler
|
|
latents = latents * self.scheduler.init_noise_sigma
|
|
return latents
|
|
|
|
def prepare_video(
|
|
self,
|
|
video,
|
|
width,
|
|
height,
|
|
batch_size,
|
|
num_videos_per_prompt,
|
|
device,
|
|
dtype,
|
|
do_classifier_free_guidance=False,
|
|
guess_mode=False,
|
|
):
|
|
video = self.control_video_processor.preprocess_video(video, height=height, width=width).to(
|
|
dtype=torch.float32
|
|
)
|
|
video = video.permute(0, 2, 1, 3, 4).flatten(0, 1)
|
|
video_batch_size = video.shape[0]
|
|
|
|
if video_batch_size == 1:
|
|
repeat_by = batch_size
|
|
else:
|
|
# image batch size is the same as prompt batch size
|
|
repeat_by = num_videos_per_prompt
|
|
|
|
video = video.repeat_interleave(repeat_by, dim=0)
|
|
video = video.to(device=device, dtype=dtype)
|
|
|
|
if do_classifier_free_guidance and not guess_mode:
|
|
video = torch.cat([video] * 2)
|
|
|
|
return video
|
|
|
|
@property
|
|
def guidance_scale(self):
|
|
return self._guidance_scale
|
|
|
|
@property
|
|
def clip_skip(self):
|
|
return self._clip_skip
|
|
|
|
# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
|
|
# of the Imagen paper: https://huggingface.co/papers/2205.11487 . `guidance_scale = 1`
|
|
# corresponds to doing no classifier free guidance.
|
|
@property
|
|
def do_classifier_free_guidance(self):
|
|
return self._guidance_scale > 1
|
|
|
|
@property
|
|
def cross_attention_kwargs(self):
|
|
return self._cross_attention_kwargs
|
|
|
|
@property
|
|
def num_timesteps(self):
|
|
return self._num_timesteps
|
|
|
|
@property
|
|
def interrupt(self):
|
|
return self._interrupt
|
|
|
|
@torch.no_grad()
|
|
def __call__(
|
|
self,
|
|
prompt: Union[str, List[str]] = None,
|
|
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[PipelineImageInput] = None,
|
|
conditioning_frames: Optional[List[PipelineImageInput]] = None,
|
|
output_type: Optional[str] = "pil",
|
|
return_dict: bool = True,
|
|
cross_attention_kwargs: Optional[Dict[str, Any]] = None,
|
|
controlnet_conditioning_scale: Union[float, List[float]] = 1.0,
|
|
guess_mode: bool = False,
|
|
control_guidance_start: Union[float, List[float]] = 0.0,
|
|
control_guidance_end: Union[float, List[float]] = 1.0,
|
|
clip_skip: Optional[int] = None,
|
|
callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None,
|
|
callback_on_step_end_tensor_inputs: List[str] = ["latents"],
|
|
decode_chunk_size: int = 16,
|
|
):
|
|
r"""
|
|
The call function to the pipeline for generation.
|
|
|
|
Args:
|
|
prompt (`str` or `List[str]`, *optional*):
|
|
The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
|
|
height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The height in pixels of the generated video.
|
|
width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`):
|
|
The width in pixels of the generated video.
|
|
num_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.
|
|
conditioning_frames (`List[PipelineImageInput]`, *optional*):
|
|
The ControlNet input condition to provide guidance to the `unet` for generation. If multiple
|
|
ControlNets are specified, images must be passed as a list such that each element of the list can be
|
|
correctly batched for input to a single ControlNet.
|
|
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).
|
|
controlnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0):
|
|
The outputs of the ControlNet are multiplied by `controlnet_conditioning_scale` before they are added
|
|
to the residual in the original `unet`. If multiple ControlNets are specified in `init`, you can set
|
|
the corresponding scale as a list.
|
|
guess_mode (`bool`, *optional*, defaults to `False`):
|
|
The ControlNet encoder tries to recognize the content of the input image even if you remove all
|
|
prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended.
|
|
control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0):
|
|
The percentage of total steps at which the ControlNet starts applying.
|
|
control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0):
|
|
The percentage of total steps at which the ControlNet stops applying.
|
|
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.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.
|
|
"""
|
|
controlnet = self.controlnet._orig_mod if is_compiled_module(self.controlnet) else self.controlnet
|
|
|
|
# align format for control guidance
|
|
if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list):
|
|
control_guidance_start = len(control_guidance_end) * [control_guidance_start]
|
|
elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list):
|
|
control_guidance_end = len(control_guidance_start) * [control_guidance_end]
|
|
elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list):
|
|
mult = len(controlnet.nets) if isinstance(controlnet, MultiControlNetModel) else 1
|
|
control_guidance_start, control_guidance_end = (
|
|
mult * [control_guidance_start],
|
|
mult * [control_guidance_end],
|
|
)
|
|
|
|
# 0. Default height and width to unet
|
|
height = height or self.unet.config.sample_size * self.vae_scale_factor
|
|
width = width or self.unet.config.sample_size * self.vae_scale_factor
|
|
|
|
num_videos_per_prompt = 1
|
|
|
|
# 1. Check inputs. Raise error if not correct
|
|
self.check_inputs(
|
|
prompt=prompt,
|
|
height=height,
|
|
width=width,
|
|
num_frames=num_frames,
|
|
negative_prompt=negative_prompt,
|
|
callback_on_step_end_tensor_inputs=callback_on_step_end_tensor_inputs,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
video=conditioning_frames,
|
|
controlnet_conditioning_scale=controlnet_conditioning_scale,
|
|
control_guidance_start=control_guidance_start,
|
|
control_guidance_end=control_guidance_end,
|
|
)
|
|
|
|
self._guidance_scale = guidance_scale
|
|
self._clip_skip = clip_skip
|
|
self._cross_attention_kwargs = cross_attention_kwargs
|
|
self._interrupt = False
|
|
|
|
# 2. Define call parameters
|
|
if prompt is not None and isinstance(prompt, (str, dict)):
|
|
batch_size = 1
|
|
elif prompt is not None and isinstance(prompt, list):
|
|
batch_size = len(prompt)
|
|
else:
|
|
batch_size = prompt_embeds.shape[0]
|
|
|
|
device = self._execution_device
|
|
|
|
if isinstance(controlnet, MultiControlNetModel) and isinstance(controlnet_conditioning_scale, float):
|
|
controlnet_conditioning_scale = [controlnet_conditioning_scale] * len(controlnet.nets)
|
|
|
|
global_pool_conditions = (
|
|
controlnet.config.global_pool_conditions
|
|
if isinstance(controlnet, ControlNetModel)
|
|
else controlnet.nets[0].config.global_pool_conditions
|
|
)
|
|
guess_mode = guess_mode or global_pool_conditions
|
|
|
|
# 3. Encode input prompt
|
|
text_encoder_lora_scale = (
|
|
cross_attention_kwargs.get("scale", None) if cross_attention_kwargs is not None else None
|
|
)
|
|
if self.free_noise_enabled:
|
|
prompt_embeds, negative_prompt_embeds = self._encode_prompt_free_noise(
|
|
prompt=prompt,
|
|
num_frames=num_frames,
|
|
device=device,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
negative_prompt=negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
else:
|
|
prompt_embeds, negative_prompt_embeds = self.encode_prompt(
|
|
prompt,
|
|
device,
|
|
num_videos_per_prompt,
|
|
self.do_classifier_free_guidance,
|
|
negative_prompt,
|
|
prompt_embeds=prompt_embeds,
|
|
negative_prompt_embeds=negative_prompt_embeds,
|
|
lora_scale=text_encoder_lora_scale,
|
|
clip_skip=self.clip_skip,
|
|
)
|
|
|
|
# For classifier free guidance, we need to do two forward passes.
|
|
# Here we concatenate the unconditional and text embeddings into a single batch
|
|
# to avoid doing two forward passes
|
|
if self.do_classifier_free_guidance:
|
|
prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds])
|
|
|
|
prompt_embeds = prompt_embeds.repeat_interleave(repeats=num_frames, dim=0)
|
|
|
|
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,
|
|
)
|
|
|
|
if isinstance(controlnet, ControlNetModel):
|
|
conditioning_frames = self.prepare_video(
|
|
video=conditioning_frames,
|
|
width=width,
|
|
height=height,
|
|
batch_size=batch_size * num_videos_per_prompt * num_frames,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
device=device,
|
|
dtype=controlnet.dtype,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
guess_mode=guess_mode,
|
|
)
|
|
elif isinstance(controlnet, MultiControlNetModel):
|
|
cond_prepared_videos = []
|
|
for frame_ in conditioning_frames:
|
|
prepared_video = self.prepare_video(
|
|
video=frame_,
|
|
width=width,
|
|
height=height,
|
|
batch_size=batch_size * num_videos_per_prompt * num_frames,
|
|
num_videos_per_prompt=num_videos_per_prompt,
|
|
device=device,
|
|
dtype=controlnet.dtype,
|
|
do_classifier_free_guidance=self.do_classifier_free_guidance,
|
|
guess_mode=guess_mode,
|
|
)
|
|
cond_prepared_videos.append(prepared_video)
|
|
conditioning_frames = cond_prepared_videos
|
|
else:
|
|
assert False
|
|
|
|
# 4. Prepare timesteps
|
|
self.scheduler.set_timesteps(num_inference_steps, device=device)
|
|
timesteps = self.scheduler.timesteps
|
|
|
|
# 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. 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
|
|
)
|
|
|
|
# 7.1 Create tensor stating which controlnets to keep
|
|
controlnet_keep = []
|
|
for i in range(len(timesteps)):
|
|
keeps = [
|
|
1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e)
|
|
for s, e in zip(control_guidance_start, control_guidance_end)
|
|
]
|
|
controlnet_keep.append(keeps[0] if isinstance(controlnet, ControlNetModel) else keeps)
|
|
|
|
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
|
|
|
|
# 8. 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)
|
|
|
|
if guess_mode and self.do_classifier_free_guidance:
|
|
# Infer ControlNet only for the conditional batch.
|
|
control_model_input = latents
|
|
control_model_input = self.scheduler.scale_model_input(control_model_input, t)
|
|
controlnet_prompt_embeds = prompt_embeds.chunk(2)[1]
|
|
else:
|
|
control_model_input = latent_model_input
|
|
controlnet_prompt_embeds = prompt_embeds
|
|
|
|
if isinstance(controlnet_keep[i], list):
|
|
cond_scale = [c * s for c, s in zip(controlnet_conditioning_scale, controlnet_keep[i])]
|
|
else:
|
|
controlnet_cond_scale = controlnet_conditioning_scale
|
|
if isinstance(controlnet_cond_scale, list):
|
|
controlnet_cond_scale = controlnet_cond_scale[0]
|
|
cond_scale = controlnet_cond_scale * controlnet_keep[i]
|
|
|
|
control_model_input = torch.transpose(control_model_input, 1, 2)
|
|
control_model_input = control_model_input.reshape(
|
|
(-1, control_model_input.shape[2], control_model_input.shape[3], control_model_input.shape[4])
|
|
)
|
|
|
|
down_block_res_samples, mid_block_res_sample = self.controlnet(
|
|
control_model_input,
|
|
t,
|
|
encoder_hidden_states=controlnet_prompt_embeds,
|
|
controlnet_cond=conditioning_frames,
|
|
conditioning_scale=cond_scale,
|
|
guess_mode=guess_mode,
|
|
return_dict=False,
|
|
)
|
|
|
|
# predict the noise residual
|
|
noise_pred = self.unet(
|
|
latent_model_input,
|
|
t,
|
|
encoder_hidden_states=prompt_embeds,
|
|
cross_attention_kwargs=self.cross_attention_kwargs,
|
|
added_cond_kwargs=added_cond_kwargs,
|
|
down_block_additional_residuals=down_block_res_samples,
|
|
mid_block_additional_residual=mid_block_res_sample,
|
|
).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, decode_chunk_size)
|
|
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 AnimateDiffPipelineOutput(frames=video)
|