#
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 |

##
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

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 MotionAdapter, AnimateDiffPipeline, DDIMScheduler
    from diffusers.utils import export_to_gif

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

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.

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

    # Load the motion adapter
    adapter = MotionAdapter.from_pretrained("guoyww/animatediff-motion-adapter-v1-5-2")
    # load SD 1.5 based finetuned model
    model_id = "SG161222/Realistic_Vision_V5.1_noVAE"
    pipe = AnimateDiffPipeline.from_pretrained(model_id, motion_adapter=adapter)
    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, 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 MotionAdapter, AnimateDiffPipeline, DDIMScheduler
    from diffusers.utils import export_to_gif

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

    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", 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.

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.

##
AnimateDiffPipeline

###
class diffusers.AnimateDiffPipeline

< source >

( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel motion_adapter: MotionAdapter 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] )

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.).

####
__call__

< source >

( prompt: typing.Union[str, typing.List[str]] = None 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.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: typing.Optional[int] = 1 cross_attention_kwargs: typing.Union[typing.Dict[str, typing.Any], NoneType] = None clip_skip: typing.Optional[int] = None ) →
TextToVideoSDPipelineOutput 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.
- 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.
- callback (`Callable`, optional) — A function that calls every `callback_steps` steps during inference. The function is called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
- callback_steps (`int`, optional, defaults to 1) — The frequency at which the `callback` function is called. If not specified, the callback is called at every step.
- 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.

Returns

TextToVideoSDPipelineOutput or `tuple`

If `return_dict` is `True`, TextToVideoSDPipelineOutput 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:

####
disable_freeu

< source >

( )

Disables the FreeU mechanism if enabled.

####
disable_vae_slicing

< source >

( )

Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to computing decoding in one step.

####
disable_vae_tiling

< source >

( )

Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to computing decoding in one step.

####
enable_freeu

< source >

( s1: float s2: float b1: float b2: float )

Parameters

- s1 (`float`) — Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate “oversmoothing effect” in the enhanced denoising process.
- s2 (`float`) — Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate “oversmoothing effect” in the enhanced denoising process.
- b1 (`float`) — Scaling factor for stage 1 to amplify the contributions of backbone features.
- b2 (`float`) — Scaling factor for stage 2 to amplify the contributions of backbone features.

Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497.

The suffixes after the scaling factors represent the stages where they are being applied.

Please refer to the official repository for combinations of the values that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL.

####
enable_vae_slicing

< source >

( )

Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps. This is useful to save some memory and allow larger batch sizes.

####
enable_vae_tiling

< source >

( )

Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow processing larger images.

####
encode_prompt

< source >

( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = 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 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.

- enable_freeu
- disable_freeu
- enable_vae_slicing
- disable_vae_slicing
- enable_vae_tiling
- disable_vae_tiling

##
AnimateDiffPipelineOutput

###
class diffusers.pipelines.animatediff.AnimateDiffPipelineOutput

< source >

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