#
Text-to-Video Generation with AnimateDiff

##
Overview

AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning by Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, Bo Dai.

The abstract of the paper is the following:

With the advance of text-to-image models (e.g., Stable Diffusion) and corresponding personalization techniques such as DreamBooth and LoRA, everyone can manifest their imagination into high-quality images at an affordable cost. Subsequently, there is a great demand for image animation techniques to further combine generated static images with motion dynamics. In this report, we propose a practical framework to animate most of the existing personalized text-to-image models once and for all, saving efforts in model-specific tuning. At the core of the proposed framework is to insert a newly initialized motion modeling module into the frozen text-to-image model and train it on video clips to distill reasonable motion priors. Once trained, by simply injecting this motion modeling module, all personalized versions derived from the same base T2I readily become text-driven models that produce diverse and personalized animated images. We conduct our evaluation on several public representative personalized text-to-image models across anime pictures and realistic photographs, and demonstrate that our proposed framework helps these models generate temporally smooth animation clips while preserving the domain and diversity of their outputs. Code and pre-trained weights will be publicly available at this https URL.

##
Available Pipelines

Pipeline | Tasks | Demo
--- | --- | ---
AnimateDiffPipeline | Text-to-Video Generation with AnimateDiff |
AnimateDiffVideoToVideoPipeline | Video-to-Video Generation with AnimateDiff |

##
Available checkpoints

Motion Adapter checkpoints can be found under guoyww. These checkpoints are meant to work with any model based on Stable Diffusion 1.4/1.5.

##
Usage example

###
AnimateDiffPipeline

AnimateDiff works with a MotionAdapter checkpoint and a Stable Diffusion 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 Stable Diffusion UNet.

The following example demonstrates how to use a MotionAdapter checkpoint with Diffusers for inference based on StableDiffusion-1.4/1.5.

    import torch
    from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
    from diffusers.utils import export_to_gif

    # Load the motion adapter
    adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
    # load SD 1.5 based finetuned model
    model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
    pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
    scheduler = DDIMScheduler.from_pretrained(
        model_id,
        subfolder="scheduler",
        clip_sample=False,
        timestep_spacing="linspace",
        beta_schedule="linear",
        steps_offset=1,
    )
    pipe.scheduler = scheduler

    # enable memory savings
    pipe.enable_vae_slicing()
    pipe.enable_model_cpu_offload()

    output = pipe(
        prompt=(
            "masterpiece, bestquality, highlydetailed, ultradetailed, sunset, "
            "orange sky, warm lighting, fishing boats, ocean waves seagulls, "
            "rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, "
            "golden hour, coastal landscape, seaside scenery"
        ),
        negative_prompt="bad quality, worse quality",
        num_frames=16,
        guidance_scale=7.5,
        num_inference_steps=25,
        generator=torch.Generator("cpu").manual_seed(42),
    )
    frames = output.frames[0]
    export_to_gif(frames, "animation.gif")

Here are some sample outputs:

masterpiece, bestquality, sunset.

AnimateDiff tends to work better with finetuned Stable Diffusion models. 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 AnimateDiff checkpoints can be sensitive to the beta schedule of the scheduler. We recommend setting this to `linear`.

###
AnimateDiffVideoToVideoPipeline

AnimateDiff can also be used to generate visually similar videos or enable style/character/background or other edits starting from an initial video, allowing you to seamlessly explore creative possibilities.

    import imageio
    import requests
    import torch
    from diffusers import AnimateDiffVideoToVideoPipeline, DDIMScheduler, MotionAdapter
    from diffusers.utils import export_to_gif
    from io import BytesIO
    from PIL import Image

    # Load the motion adapter
    adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
    # load SD 1.5 based finetuned model
    model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
    pipe = AnimateDiffVideoToVideoPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to("cuda")
    scheduler = DDIMScheduler.from_pretrained(
        model_id,
        subfolder="scheduler",
        clip_sample=False,
        timestep_spacing="linspace",
        beta_schedule="linear",
        steps_offset=1,
    )
    pipe.scheduler = scheduler

    # enable memory savings
    pipe.enable_vae_slicing()
    pipe.enable_model_cpu_offload()

    # helper function to load videos
    def load_video(file_path: str):
        images = []

        if file_path.startswith(('http://', 'https://')):
            # If the file_path is a URL
            response = requests.get(file_path)
            response.raise_for_status()
            content = BytesIO(response.content)
            vid = imageio.get_reader(content)
        else:
            # Assuming it's a local file path
            vid = imageio.get_reader(file_path)

        for frame in vid:
            pil_image = Image.fromarray(frame)
            images.append(pil_image)

        return images

    video = load_video("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/animatediff-vid2vid-input-1.gif")

    output = pipe(
        video = video,
        prompt="panda playing a guitar, on a boat, in the ocean, high quality",
        negative_prompt="bad quality, worse quality",
        guidance_scale=7.5,
        num_inference_steps=25,
        strength=0.5,
        generator=torch.Generator("cpu").manual_seed(42),
    )
    frames = output.frames[0]
    export_to_gif(frames, "animation.gif")

Here are some sample outputs:

Source Video | Output Video
--- | ---
raccoon playing a guitar | panda playing a guitar
closeup of margot robbie, fireworks in the background, high quality | closeup of tony stark, robert downey jr, fireworks

##
Using Motion LoRAs

Motion LoRAs are a collection of LoRAs that work with the `guoyww/animatediff-motion-adapter-v1-5-2` checkpoint. These LoRAs are responsible for adding specific types of motion to the animations.

    import torch
    from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
    from diffusers.utils import export_to_gif

    # Load the motion adapter
    adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
    # load SD 1.5 based finetuned model
    model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
    pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)
    pipe.load_lora_weights(
        "guoyww/animatediff-motion-lora-zoom-out", adapter_name="zoom-out"
    )

    scheduler = DDIMScheduler.from_pretrained(
        model_id,
        subfolder="scheduler",
        clip_sample=False,
        beta_schedule="linear",
        timestep_spacing="linspace",
        steps_offset=1,
    )
    pipe.scheduler = scheduler

    # enable memory savings
    pipe.enable_vae_slicing()
    pipe.enable_model_cpu_offload()

    output = pipe(
        prompt=(
            "masterpiece, bestquality, highlydetailed, ultradetailed, sunset, "
            "orange sky, warm lighting, fishing boats, ocean waves seagulls, "
            "rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, "
            "golden hour, coastal landscape, seaside scenery"
        ),
        negative_prompt="bad quality, worse quality",
        num_frames=16,
        guidance_scale=7.5,
        num_inference_steps=25,
        generator=torch.Generator("cpu").manual_seed(42),
    )
    frames = output.frames[0]
    export_to_gif(frames, "animation.gif")

masterpiece, bestquality, sunset.

##
Using Motion LoRAs with PEFT

You can also leverage the PEFT backend to combine Motion LoRA’s and create more complex animations.

First install PEFT with

    pip install peft

Then you can use the following code to combine Motion LoRAs.

    import torch
    from diffusers import AnimateDiffPipeline, DDIMScheduler, MotionAdapter
    from diffusers.utils import export_to_gif

    # Load the motion adapter
    adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2", torch_dtype=torch.float16)
    # load SD 1.5 based finetuned model
    model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
    pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16)

    pipe.load_lora_weights(
        "diffusers/animatediff-motion-lora-zoom-out", adapter_name="zoom-out",
    )
    pipe.load_lora_weights(
        "diffusers/animatediff-motion-lora-pan-left", adapter_name="pan-left",
    )
    pipe.set_adapters(["zoom-out", "pan-left"], adapter_weights=[1.0, 1.0])

    scheduler = DDIMScheduler.from_pretrained(
        model_id,
        subfolder="scheduler",
        clip_sample=False,
        timestep_spacing="linspace",
        beta_schedule="linear",
        steps_offset=1,
    )
    pipe.scheduler = scheduler

    # enable memory savings
    pipe.enable_vae_slicing()
    pipe.enable_model_cpu_offload()

    output = pipe(
        prompt=(
            "masterpiece, bestquality, highlydetailed, ultradetailed, sunset, "
            "orange sky, warm lighting, fishing boats, ocean waves seagulls, "
            "rippling water, wharf, silhouette, serene atmosphere, dusk, evening glow, "
            "golden hour, coastal landscape, seaside scenery"
        ),
        negative_prompt="bad quality, worse quality",
        num_frames=16,
        guidance_scale=7.5,
        num_inference_steps=25,
        generator=torch.Generator("cpu").manual_seed(42),
    )
    frames = output.frames[0]
    export_to_gif(frames, "animation.gif")

masterpiece, bestquality, sunset.

##
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 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 MotionAdapter, AnimateDiffPipeline, DDIMScheduler
    from diffusers.utils import export_to_gif

    adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
    model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
    pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter, torch_dtype=torch.float16).to("cuda")
    pipe.scheduler = DDIMScheduler.from_pretrained(
        model_id,
        subfolder="scheduler",
        beta_schedule="linear",
        clip_sample=False,
        timestep_spacing="linspace",
        steps_offset=1
    )

    # enable memory savings
    pipe.enable_vae_slicing()
    pipe.enable_vae_tiling()

    # enable FreeInit
    # Refer to the enable_free_init documentation for a full list of configurable parameters
    pipe.enable_free_init(method="butterworth", use_fast_sampling=True)

    # run inference
    output = pipe(
        prompt="a panda playing a guitar, on a boat, in the ocean, high quality",
        negative_prompt="bad quality, worse quality",
        num_frames=16,
        guidance_scale=7.5,
        num_inference_steps=20,
        generator=torch.Generator("cpu").manual_seed(666),
    )

    # disable FreeInit
    pipe.disable_free_init()

    frames = output.frames[0]
    export_to_gif(frames, "animation.gif")

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).

Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

Without FreeInit enabled | With FreeInit enabled
--- | ---
panda playing a guitar | panda playing a guitar

##
Using AnimateLCM

AnimateLCM is a motion module checkpoint and an LCM LoRA that have been created using a consistency learning strategy that decouples the distillation of the image generation priors and the motion generation priors.

    import torch
    from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter
    from diffusers.utils import export_to_gif

    adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM")
    pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter)
    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")

    pipe.load_lora_weights("wangfuyun/AnimateLCM", weight_name="sd15_lora_beta.safetensors", adapter_name="lcm-lora")

    pipe.enable_vae_slicing()
    pipe.enable_model_cpu_offload()

    output = pipe(
        prompt="A space rocket with trails of smoke behind it launching into space from the desert, 4k, high resolution",
        negative_prompt="bad quality, worse quality, low resolution",
        num_frames=16,
        guidance_scale=1.5,
        num_inference_steps=6,
        generator=torch.Generator("cpu").manual_seed(0),
    )
    frames = output.frames[0]
    export_to_gif(frames, "animatelcm.gif")

A space rocket, 4K.

AnimateLCM is also compatible with existing Motion LoRAs.

    import torch
    from diffusers import AnimateDiffPipeline, LCMScheduler, MotionAdapter
    from diffusers.utils import export_to_gif

    adapter = MotionAdapter.from_pretrained("wangfuyun/AnimateLCM")
    pipe = AnimateDiffPipeline.from_pretrained("emilianJR/epiCRealism", motion_adapter=adapter)
    pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config, beta_schedule="linear")

    pipe.load_lora_weights("wangfuyun/AnimateLCM", weight_name="sd15_lora_beta.safetensors", adapter_name="lcm-lora")
    pipe.load_lora_weights("guoyww/animatediff-motion-lora-tilt-up", adapter_name="tilt-up")

    pipe.set_adapters(["lcm-lora", "tilt-up"], [1.0, 0.8])
    pipe.enable_vae_slicing()
    pipe.enable_model_cpu_offload()

    output = pipe(
        prompt="A space rocket with trails of smoke behind it launching into space from the desert, 4k, high resolution",
        negative_prompt="bad quality, worse quality, low resolution",
        num_frames=16,
        guidance_scale=1.5,
        num_inference_steps=6,
        generator=torch.Generator("cpu").manual_seed(0),
    )
    frames = output.frames[0]
    export_to_gif(frames, "animatelcm-motion-lora.gif")

A space rocket, 4K.

##
AnimateDiffPipeline

###
class diffusers.AnimateDiffPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: Union motion_adapter: MotionAdapter scheduler: Union 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 of DDIMScheduler, 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__

< source >

( prompt: Union = None num_frames: Optional = 16 height: Optional = None width: Optional = None num_inference_steps: int = 50 guidance_scale: float = 7.5 negative_prompt: Union = None num_videos_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None ip_adapter_image: Union = None ip_adapter_image_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True cross_attention_kwargs: Optional = None clip_skip: Optional = None callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) →
AnimateDiffPipelineOutput or `tuple`

Parameters

- 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 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.FloatTensor`, 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.FloatTensor`, 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.FloatTensor`, 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.FloatTensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generated video. Choose between `torch.FloatTensor`, `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 in `self.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 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.

Returns

AnimateDiffPipelineOutput or `tuple`

If `return_dict` is `True`, AnimateDiffPipelineOutput is returned, otherwise a `tuple` 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 MotionAdapter, AnimateDiffPipeline, DDIMScheduler
    >>> from diffusers.utils import export_to_gif

    >>> adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
    >>> pipe = AnimateDiffPipeline.from_pretrained("frankjoshua/toonyou_beta6", motion_adapter=adapter)
    >>> pipe.scheduler = DDIMScheduler(beta_schedule="linear", steps_offset=1, clip_sample=False)
    >>> output = pipe(prompt="A corgi walking in the park")
    >>> frames = output.frames[0]
    >>> export_to_gif(frames, "animation.gif")

####
encode_prompt

< source >

( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = 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 pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- prompt_embeds (`torch.FloatTensor`, 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.FloatTensor`, 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.

##
AnimateDiffVideoToVideoPipeline

###
class diffusers.AnimateDiffVideoToVideoPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel motion_adapter: MotionAdapter scheduler: Union 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 of DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.

Pipeline for video-to-video generation.

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

The pipeline also inherits the following loading methods:

- 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__

< source >

( video: List = None prompt: Union = None height: Optional = None width: Optional = None num_inference_steps: int = 50 timesteps: Optional = None guidance_scale: float = 7.5 strength: float = 0.8 negative_prompt: Union = None num_videos_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None latents: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None ip_adapter_image: Union = None ip_adapter_image_embeds: Optional = None output_type: Optional = 'pil' return_dict: bool = True cross_attention_kwargs: Optional = None clip_skip: Optional = None callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] ) →
pipelines.animatediff.pipeline_output.AnimateDiffPipelineOutput or `tuple`

Parameters

- video (`List[PipelineImageInput]`) — The input video to condition the generation on. Must be a list of images/frames of the video.
- prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`.
- height (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) — The height in pixels of the generated video.
- width (`int`, optional, defaults to `self.unet.config.sample_size * self.vae_scale_factor`) — The width in pixels of the generated video.
- num_inference_steps (`int`, optional, defaults to 50) — The number of denoising steps. More denoising steps usually lead to a higher quality videos at the expense of slower inference.
- strength (`float`, optional, defaults to 0.8) — Higher strength leads to more differences between original video and generated video.
- guidance_scale (`float`, optional, defaults to 7.5) — A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`.
- negative_prompt (`str` or `List[str]`, optional) — The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`).
- eta (`float`, optional, defaults to 0.0) — Corresponds to parameter eta (η) from the DDIM 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.FloatTensor`, 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.FloatTensor`, 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.FloatTensor`, 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.FloatTensor]`, optional) — Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument.
- output_type (`str`, optional, defaults to `"pil"`) — The output format of the generated video. Choose between `torch.FloatTensor`, `PIL.Image` or `np.array`.
- return_dict (`bool`, optional, defaults to `True`) — Whether or not to return a `AnimateDiffPipelineOutput` instead of a plain tuple.
- cross_attention_kwargs (`dict`, optional) — A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined in `self.processor`.
- 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.

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.

The call function to the pipeline for generation.

Examples:

####
encode_prompt

< source >

( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = 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 pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`).
- prompt_embeds (`torch.FloatTensor`, 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.FloatTensor`, 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.

##
AnimateDiffPipelineOutput

###
class diffusers.pipelines.animatediff.AnimateDiffPipelineOutput

< source >

( frames: Union )

Parameters

- frames (`torch.Tensor`, `np.ndarray`, or List[List[PIL.Image.Image]]) — List of video outputs - It can be a nested list of length `batch_size,` with each sub-list containing denoised

Output class for AnimateDiff pipelines.

PIL image sequences of length `num_frames.` It can also be a NumPy array or Torch tensor of shape `(batch_size, num_frames, channels, height, width)`