Image-to-Video Generation with PIA (Personalized Image Animator) (original) (raw)

Overview

PIA: Your Personalized Image Animator via Plug-and-Play Modules in Text-to-Image Models by Yiming Zhang, Zhening Xing, Yanhong Zeng, Youqing Fang, Kai Chen

Recent advancements in personalized text-to-image (T2I) models have revolutionized content creation, empowering non-experts to generate stunning images with unique styles. While promising, adding realistic motions into these personalized images by text poses significant challenges in preserving distinct styles, high-fidelity details, and achieving motion controllability by text. In this paper, we present PIA, a Personalized Image Animator that excels in aligning with condition images, achieving motion controllability by text, and the compatibility with various personalized T2I models without specific tuning. To achieve these goals, PIA builds upon a base T2I model with well-trained temporal alignment layers, allowing for the seamless transformation of any personalized T2I model into an image animation model. A key component of PIA is the introduction of the condition module, which utilizes the condition frame and inter-frame affinity as input to transfer appearance information guided by the affinity hint for individual frame synthesis in the latent space. This design mitigates the challenges of appearance-related image alignment within and allows for a stronger focus on aligning with motion-related guidance.

Project page

Available Pipelines

Available checkpoints

Motion Adapter checkpoints for PIA can be found under the OpenMMLab org. These checkpoints are meant to work with any model based on Stable Diffusion 1.5

Usage example

PIA works with a MotionAdapter checkpoint and a Stable Diffusion 1.5 model checkpoint. The MotionAdapter is a collection of Motion Modules that are responsible for adding coherent motion across image frames. These modules are applied after the Resnet and Attention blocks in the Stable Diffusion UNet. In addition to the motion modules, PIA also replaces the input convolution layer of the SD 1.5 UNet model with a 9 channel input convolution layer.

The following example demonstrates how to use PIA to generate a video from a single image.

import torch from diffusers import ( EulerDiscreteScheduler, MotionAdapter, PIAPipeline, ) from diffusers.utils import export_to_gif, load_image

adapter = MotionAdapter.from_pretrained("openmmlab/PIA-condition-adapter") pipe = PIAPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter, torch_dtype=torch.float16)

pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) pipe.enable_model_cpu_offload() pipe.enable_vae_slicing()

image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true" ) image = image.resize((512, 512)) prompt = "cat in a field" negative_prompt = "wrong white balance, dark, sketches,worst quality,low quality"

generator = torch.Generator("cpu").manual_seed(0) output = pipe(image=image, prompt=prompt, generator=generator) frames = output.frames[0] export_to_gif(frames, "pia-animation.gif")

Here are some sample outputs:

cat in a field.

If you plan on using a scheduler that can clip samples, make sure to disable it by setting clip_sample=False in the scheduler as this can also have an adverse effect on generated samples. Additionally, the PIA checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to linear.

Using FreeInit

FreeInit: Bridging Initialization Gap in Video Diffusion Models by Tianxing Wu, Chenyang Si, Yuming Jiang, Ziqi Huang, Ziwei Liu.

FreeInit is an effective method that improves temporal consistency and overall quality of videos generated using video-diffusion-models without any addition training. It can be applied to PIA, AnimateDiff, ModelScope, VideoCrafter and various other video generation models seamlessly at inference time, and works by iteratively refining the latent-initialization noise. More details can be found it the paper.

The following example demonstrates the usage of FreeInit.

import torch from diffusers import ( DDIMScheduler, MotionAdapter, PIAPipeline, ) from diffusers.utils import export_to_gif, load_image

adapter = MotionAdapter.from_pretrained("openmmlab/PIA-condition-adapter") pipe = PIAPipeline.from_pretrained("SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter)

pipe.enable_free_init(method="butterworth", use_fast_sampling=True)

pipe.enable_model_cpu_offload() pipe.enable_vae_slicing()

pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) image = load_image( "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true" ) image = image.resize((512, 512)) prompt = "cat in a field" negative_prompt = "wrong white balance, dark, sketches,worst quality,low quality"

generator = torch.Generator("cpu").manual_seed(0)

output = pipe(image=image, prompt=prompt, generator=generator) frames = output.frames[0] export_to_gif(frames, "pia-freeinit-animation.gif")

cat in a field.

FreeInit is not really free - the improved quality comes at the cost of extra computation. It requires sampling a few extra times depending on the num_iters parameter that is set when enabling it. Setting the use_fast_sampling parameter to True can improve the overall performance (at the cost of lower quality compared to when use_fast_sampling=False but still better results than vanilla video generation models).

PIAPipeline

class diffusers.PIAPipeline

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( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: typing.Union[diffusers.models.unets.unet_2d_condition.UNet2DConditionModel, diffusers.models.unets.unet_motion_model.UNetMotionModel] scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] motion_adapter: typing.Optional[diffusers.models.unets.unet_motion_model.MotionAdapter] = None feature_extractor: CLIPImageProcessor = None image_encoder: CLIPVisionModelWithProjection = None )

Parameters

• vae (AutoencoderKL) — Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
• text_encoder (CLIPTextModel) — Frozen text-encoder (clip-vit-large-patch14).
• tokenizer (CLIPTokenizer) — A CLIPTokenizer to tokenize text.
• unet (UNet2DConditionModel) — A UNet2DConditionModel used to create a UNetMotionModel to denoise the encoded video latents.
• motion_adapter (MotionAdapter) — A MotionAdapter to be used in combination with unet to denoise the encoded video latents.
• scheduler (SchedulerMixin) — A scheduler to be used in combination with unet to denoise the encoded image latents. Can be one ofDDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.

Pipeline for text-to-video generation.

This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.).

The pipeline also inherits the following loading methods:

• load_textual_inversion() for loading textual inversion embeddings
• load_lora_weights() for loading LoRA weights
• save_lora_weights() for saving LoRA weights
• load_ip_adapter() for loading IP Adapters

__call__

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( image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor]] prompt: typing.Union[str, typing.List[str]] = None strength: float = 1.0 num_frames: typing.Optional[int] = 16 height: typing.Optional[int] = None width: typing.Optional[int] = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: typing.Union[str, typing.List[str], NoneType] = None num_videos_per_prompt: typing.Optional[int] = 1 eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.Tensor] = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None ip_adapter_image: typing.Union[PIL.Image.Image, numpy.ndarray, torch.Tensor, typing.List[PIL.Image.Image], typing.List[numpy.ndarray], typing.List[torch.Tensor], NoneType] = None ip_adapter_image_embeds: typing.Optional[typing.List[torch.Tensor]] = None motion_scale: int = 0 output_type: typing.Optional[str] = 'pil' return_dict: bool = True cross_attention_kwargs: typing.Optional[typing.Dict[str, typing.Any]] = None clip_skip: typing.Optional[int] = None callback_on_step_end: typing.Optional[typing.Callable[[int, int, typing.Dict], NoneType]] = None callback_on_step_end_tensor_inputs: typing.List[str] = ['latents'] ) → PIAPipelineOutput or tuple

Parameters

• 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 textprompt 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 paper. Only applies to the DDIMScheduler, and is ignored in other schedulers.
• generator (torch.Generator or List[torch.Generator], optional) — A torch.Generator 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 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 inself.processor.
• 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 bycallback_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.

Returns

PIAPipelineOutput or tuple

If return_dict is True, PIAPipelineOutput is returned, otherwise atuple is returned where the first element is a list with the generated frames.

The call function to the pipeline for generation.

Examples:

import torch from diffusers import EulerDiscreteScheduler, MotionAdapter, PIAPipeline from diffusers.utils import export_to_gif, load_image

adapter = MotionAdapter.from_pretrained("openmmlab/PIA-condition-adapter") pipe = PIAPipeline.from_pretrained( ... "SG161222/Realistic_Vision_V6.0_B1_noVAE", motion_adapter=adapter, torch_dtype=torch.float16 ... )

pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) image = load_image( ... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/pix2pix/cat_6.png?download=true" ... ) image = image.resize((512, 512)) prompt = "cat in a hat" negative_prompt = "wrong white balance, dark, sketches, worst quality, low quality, deformed, distorted" generator = torch.Generator("cpu").manual_seed(0) output = pipe(image=image, prompt=prompt, negative_prompt=negative_prompt, generator=generator) frames = output.frames[0] export_to_gif(frames, "pia-animation.gif")

encode_prompt

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( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: typing.Optional[torch.Tensor] = None negative_prompt_embeds: typing.Optional[torch.Tensor] = None lora_scale: typing.Optional[float] = None clip_skip: typing.Optional[int] = None )

Parameters

• prompt (str or List[str], optional) — prompt to be encoded
• device — (torch.device): torch device
• num_images_per_prompt (int) — number of images that should be generated per prompt
• do_classifier_free_guidance (bool) — whether to use classifier free guidance or not
• negative_prompt (str or List[str], optional) — The prompt or prompts not to guide the image generation. If not defined, one has to passnegative_prompt_embeds instead. Ignored when not using guidance (i.e., ignored if guidance_scale is less than 1).
• prompt_embeds (torch.Tensor, optional) — Pre-generated text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, text embeddings will be generated from prompt input argument.
• negative_prompt_embeds (torch.Tensor, optional) — Pre-generated negative text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting. If not provided, negative_prompt_embeds will be generated from negative_prompt input argument.
• lora_scale (float, optional) — A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded.
• clip_skip (int, optional) — Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings.

Encodes the prompt into text encoder hidden states.

• enable_freeu
• disable_freeu
• enable_free_init
• disable_free_init
• enable_vae_slicing
• disable_vae_slicing
• enable_vae_tiling
• disable_vae_tiling

PIAPipelineOutput

class diffusers.pipelines.pia.PIAPipelineOutput

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( frames: typing.Union[torch.Tensor, numpy.ndarray, typing.List[typing.List[PIL.Image.Image]]] )

Parameters

• frames (torch.Tensor, np.ndarray, or List[List[PIL.Image.Image]]) — Nested list of length batch_size with denoised PIL image sequences of length num_frames, NumPy array of shape (batch_size, num_frames, channels, height, width, Torch tensor of shape (batch_size, num_frames, channels, height, width)`.

Output class for PIAPipeline.

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