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sayakpaul
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diffusers-qa-chatbot-artifacts

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latent code of generated videos and a list of bools indicating whether the corresponding generated
video contains β€œnot-safe-for-work” (nsfw) content..
The call function to the pipeline for generation. backward_loop < source > ( latents timesteps prompt_embeds guidance_scale callback callback_steps num_warmup_steps extra_step_kwargs cross_attention_kwargs = None ) β†’ latents Parameters callback (Callable, optional) β€”
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) β€”
The frequency at which the callback function is called. If not specified, the callback is called at
every step.
extra_step_kwargs β€”
Extra_step_kwargs.
cross_attention_kwargs β€”
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in
self.processor.
num_warmup_steps β€”
number of warmup steps. Returns
latents
Latents of backward process output at time timesteps[-1].
Perform backward process given list of time steps. 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) β€”
prompt to be encoded
device β€” (torch.device):
torch device num_images_per_prompt (int) β€”
number of images that should be generated per prompt do_classifier_free_guidance (bool) β€”
whether to use classifier free guidance or not negative_prompt (str or List[str], optional) β€”
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) β€”
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) β€”
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) β€”
A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (int, optional) β€”
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. forward_loop < source > ( x_t0 t0 t1 generator ) β†’ x_t1 Parameters generator (torch.Generator or List[torch.Generator], optional) β€”
A torch.Generator to make
generation deterministic. Returns
x_t1
Forward process applied to x_t0 from time t0 to t1.
Perform DDPM forward process from time t0 to t1. This is the same as adding noise with corresponding variance. TextToVideoZeroSDXLPipeline class diffusers.TextToVideoZeroSDXLPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers image_encoder: CLIPVisionModelWithProjection = None feature_extractor: CLIPImageProcessor = None force_zeros_for_empty_prompt: bool = True add_watermarker: Optional = None ) Parameters vae (AutoencoderKL) β€”
Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. text_encoder (CLIPTextModel) β€”
Frozen text-encoder. Stable Diffusion XL uses the text portion of
CLIP, specifically
the clip-vit-large-patch14 variant. text_encoder_2 ( CLIPTextModelWithProjection) β€”
Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of
CLIP,
specifically the
laion/CLIP-ViT-bigG-14-laion2B-39B-b160k
variant. tokenizer (CLIPTokenizer) β€”
Tokenizer of class
CLIPTokenizer. tokenizer_2 (CLIPTokenizer) β€”
Second Tokenizer of class
CLIPTokenizer. unet (UNet2DConditionModel) β€” Conditional U-Net architecture to denoise the encoded image latents. scheduler (SchedulerMixin) β€”
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 zero-shot text-to-video generation using Stable Diffusion XL. 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: Union prompt_2: Union = None video_length: Optional = 8 height: Optional = None width: Optional = None num_inference_steps: int = 50 denoising_end: Optional = None guidance_scale: float = 7.5 negative_prompt: Union = None negative_prompt_2: Union = None num_videos_per_prompt: Optional = 1 eta: float = 0.0 generator: Union = None frame_ids: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None latents: Optional = None motion_field_strength_x: float = 12 motion_field_strength_y: float = 12 output_type: Optional = 'tensor' return_dict: bool = True callback: Optional = None callback_steps: int = 1 cross_attention_kwargs: Optional = None guidance_rescale: float = 0.0 original_size: Optional = None crops_coords_top_left: Tuple = (0, 0) target_size: Optional = None t0: int = 44 t1: int = 47 ) Parameters prompt (str or List[str], optional) β€”
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds.
instead. prompt_2 (str or List[str], optional) β€”
The prompt or prompts to be sent to the tokenizer_2 and text_encoder_2. If not defined, prompt is
used in both text-encoders video_length (int, optional, defaults to 8) β€”
The number of generated video frames. height (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The height in pixels of the generated image. width (int, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) β€”
The width in pixels of the generated image. num_inference_steps (int, optional, defaults to 50) β€”
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. denoising_end (float, optional) β€”
When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be
completed before it is intentionally prematurely terminated. As a result, the returned sample will
still retain a substantial amount of noise as determined by the discrete timesteps selected by the
scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a
β€œMixture of Denoisers” multi-pipeline setup, as elaborated in Refining the Image
Output guidance_scale (float, optional, defaults to 7.5) β€”
Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scale is defined as w of equation 2. of Imagen
Paper. Guidance scale is enabled by setting guidance_scale > 1. Higher guidance scale encourages to generate images that are closely linked to the text prompt,
usually at the expense of lower image quality. negative_prompt (str or List[str], optional) β€”
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). negative_prompt_2 (str or List[str], optional) β€”
The prompt or prompts not to guide the image generation to be sent to tokenizer_2 and
text_encoder_2. If not defined, negative_prompt is used in both text-encoders num_videos_per_prompt (int, optional, defaults to 1) β€”
The number of videos to generate per prompt. eta (float, optional, defaults to 0.0) β€”
Corresponds to parameter eta (Ξ·) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
schedulers.DDIMScheduler, will be ignored for others. generator (torch.Generator or List[torch.Generator], optional) β€”
One or a list of torch generator(s)
to make generation deterministic. frame_ids (List[int], optional) β€”
Indexes of the frames that are being generated. This is used when generating longer videos
chunk-by-chunk. prompt_embeds (torch.FloatTensor, optional) β€”
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) β€”
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. pooled_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt weighting.
If not provided, pooled text embeddings will be generated from prompt input argument. negative_pooled_prompt_embeds (torch.FloatTensor, optional) β€”
Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, e.g. prompt
weighting. If not provided, pooled negative_prompt_embeds will be generated from negative_prompt
input argument. latents (torch.FloatTensor, optional) β€”
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random generator. motion_field_strength_x (float, optional, defaults to 12) β€”