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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 cross_attention_kwargs: Optional = None clip_skip: Optional = None callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) → 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. 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 pipeine class. 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_free_init < source > ( ) Disables the FreeInit mechanism if enabled. 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_free_init < source > ( num_iters: int = 3 use_fast_sampling: bool = False method: str = 'butterworth' order: int = 4 spatial_stop_frequency: float = 0.25 temporal_stop_frequency: float = 0.25 generator: Generator = None ) Parameters num_iters (int, optional, defaults to 3) — |
Number of FreeInit noise re-initialization iterations. use_fast_sampling (bool, optional, defaults to False) — |
Whether or not to speedup sampling procedure at the cost of probably lower quality results. Enables |
the “Coarse-to-Fine Sampling” strategy, as mentioned in the paper, if set to True. method (str, optional, defaults to butterworth) — |
Must be one of butterworth, ideal or gaussian to use as the filtering method for the |
FreeInit low pass filter. order (int, optional, defaults to 4) — |
Order of the filter used in butterworth method. Larger values lead to ideal method behaviour |
whereas lower values lead to gaussian method behaviour. spatial_stop_frequency (float, optional, defaults to 0.25) — |
Normalized stop frequency for spatial dimensions. Must be between 0 to 1. Referred to as d_s in |
the original implementation. temporal_stop_frequency (float, optional, defaults to 0.25) — |
Normalized stop frequency for temporal dimensions. Must be between 0 to 1. Referred to as d_t in |
the original implementation. generator (torch.Generator, optional, defaults to 0.25) — |
A torch.Generator to make |
FreeInit generation deterministic. Enables the FreeInit mechanism as in https://arxiv.org/abs/2312.07537. This implementation has been adapted from the official repository. 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. 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) — |
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