scrapped_outputs/004a80e3475d06e8d1f59f3264b0d35b.txt

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.   Copied 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.  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.   Copied 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   Copied pip install peft Then you can use the following code to combine Motion LoRAs.   Copied 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.
          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: 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 output_type: Optional = 'pil' return_dict: bool = True callback: Optional = None callback_steps: Optional = 1 cross_attention_kwargs: Optional = None clip_skip: Optional = 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.
ip_adapter_image — (PipelineImageInput, optional): Optional image input to work with IP Adapters.   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:   Copied >>> 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")  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: 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. enable_freeu disable_freeu enable_vae_slicing disable_vae_slicing enable_vae_tiling disable_vae_tiling  AnimateDiffPipelineOutput  class diffusers.pipelines.animatediff.AnimateDiffPipelineOutput  < source > ( frames: Union  )