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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/2309.114... |
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.AnimateDiffP... |
Cycle Diffusion Cycle Diffusion is a text guided image-to-image generation model proposed in Unifying Diffusion Models’ Latent Space, with Applications to CycleDiffusion and Guidance by Chen Henry Wu, Fernando De la Torre. The abstract from the paper is: Diffusion models have achieved unprecedented performance in gener... |
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 to denoise the encoded image latents. scheduler (SchedulerMixin) — |
A scheduler to be used in combination with unet to denoise the encoded image latents. Can only be an |
instance of DDIMScheduler. safety_checker (StableDiffusionSafetyChecker) — |
Classification module that estimates whether generated images could be considered offensive or harmful. |
Please refer to the model card for more details |
about a model’s potential harms. feature_extractor (CLIPImageProcessor) — |
A CLIPImageProcessor to extract features from generated images; used as inputs to the safety_checker. Pipeline for text-guided image to image generation using Stable Diffusion. 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 __call__ < sou... |
The prompt or prompts to guide the image generation. image (torch.FloatTensor np.ndarray, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) — |
Image or tensor representing an image batch to be used as the starting point. Can also accept image |
latents as image, but if passing latents directly it is not encoded again. strength (float, optional, defaults to 0.8) — |
Indicates extent to transform the reference image. Must be between 0 and 1. image is used as a |
starting point and more noise is added the higher the strength. The number of denoising steps depends |
on the amount of noise initially added. When strength is 1, added noise is maximum and the denoising |
process runs for the full number of iterations specified in num_inference_steps. A value of 1 |
essentially ignores image. num_inference_steps (int, optional, defaults to 50) — |
The number of denoising steps. More denoising steps usually lead to a higher quality image at the |
expense of slower inference. This parameter is modulated by strength. 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. source_guidance_scale (float, optional, defaults to 1) — |
Guidance scale for the source prompt. This is useful to control the amount of influence the source |
prompt has for encoding. num_images_per_prompt (int, optional, defaults to 1) — |
The number of images to generate per prompt. 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. 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 image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) — |
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