text stringlengths 0 5.54k |
|---|
Strength of motion in generated video along x-axis. See the paper, |
Sect. 3.3.1. motion_field_strength_y (float, optional, defaults to 12) — |
Strength of motion in generated video along y-axis. See the paper, |
Sect. 3.3.1. output_type (str, optional, defaults to "pil") — |
The output format of the generate image. Choose between |
PIL: PIL.Image.Image or np.array. return_dict (bool, optional, defaults to True) — |
Whether or not to return a ~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput instead |
of a plain tuple. callback (Callable, optional) — |
A function that will be called every callback_steps steps during inference. The function will be |
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 will be called. If not specified, the callback will be |
called at every step. cross_attention_kwargs (dict, optional) — |
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under |
self.processor in |
diffusers.cross_attention. guidance_rescale (float, optional, defaults to 0.7) — |
Guidance rescale factor proposed by Common Diffusion Noise Schedules and Sample Steps are |
Flawed guidance_scale is defined as φ in equation 16. of |
Common Diffusion Noise Schedules and Sample Steps are Flawed. |
Guidance rescale factor should fix overexposure when using zero terminal SNR. original_size (Tuple[int], optional, defaults to (1024, 1024)) — |
If original_size is not the same as target_size the image will appear to be down- or upsampled. |
original_size defaults to (width, height) if not specified. Part of SDXL’s micro-conditioning as |
explained in section 2.2 of |
https://huggingface.co/papers/2307.01952. crops_coords_top_left (Tuple[int], optional, defaults to (0, 0)) — |
crops_coords_top_left can be used to generate an image that appears to be “cropped” from the position |
crops_coords_top_left downwards. Favorable, well-centered images are usually achieved by setting |
crops_coords_top_left to (0, 0). Part of SDXL’s micro-conditioning as explained in section 2.2 of |
https://huggingface.co/papers/2307.01952. target_size (Tuple[int], optional, defaults to (1024, 1024)) — |
For most cases, target_size should be set to the desired height and width of the generated image. If |
not specified it will default to (width, height). Part of SDXL’s micro-conditioning as explained in |
section 2.2 of https://huggingface.co/papers/2307.01952. t0 (int, optional, defaults to 44) — |
Timestep t0. Should be in the range [0, num_inference_steps - 1]. See the |
paper, Sect. 3.3.1. t1 (int, optional, defaults to 47) — |
Timestep t0. Should be in the range [t0 + 1, num_inference_steps - 1]. See the |
paper, Sect. 3.3.1. Function invoked when calling the pipeline for generation. backward_loop < source > ( latents timesteps prompt_embeds guidance_scale callback callback_steps num_warmup_steps extra_step_kwargs add_text_embeds add_time_ids cross_attention_kwargs = None guidance_rescale: float = 0.0 ) → latents ... |
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. |
extra_step_kwargs — |
Extra_step_kwargs. |
cross_attention_kwargs — |
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in |
self.processor. |
num_warmup_steps — |
number of warmup steps. Returns |
latents |
latents of backward process output at time timesteps[-1] |
Perform backward process given list of time steps 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. 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. encode_prompt < source > ( prompt: str prompt_2: Optional = None device: Optional = None num_images_per_prompt: int = 1 do_classifier_free_guidance: bool = True negative_prompt: Optional = None negative_prompt_2: Optio... |
prompt to be encoded prompt_2 (str or List[str], optional) — |
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 |
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). negative_prompt_2 (str or List[str], optional) — |
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 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. pooled_prompt_embeds (torch.FloatTensor, optional) — |
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) — |
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. 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. forward_loop < source > ( x_t0 t0 t1 generator ) → x_t1 Parameters generator (torch.Generator or List[torch.Generator], optional) — |
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. TextToVideoPipelineOutput class diffusers.pipelines.text_to_video_synthesis.pipeline_text_to_video_zero.TextToVideoPipelineOutput < source > ( images: Union nsfw_content_detected: Optional ) Parameters... |
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). nsfw_content_detected ([List[bool]]) — |
List indicating whether the corresponding generated image contains “not-safe-for-work” (nsfw) content or |
None if safety checking could not be performed. Output class for zero-shot text-to-video pipeline. |
ControlNet with Stable Diffusion XL ControlNet was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you pro... |
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). text_encoder_2 (CLIPTextModelWithProjection) — |
Second frozen text-encoder |
(laion/CLIP-ViT-bigG-14-laion2B-39B-b160k). tokenizer (CLIPTokenizer) — |
A CLIPTokenizer to tokenize text. tokenizer_2 (CLIPTokenizer) — |
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) — |
A UNet2DConditionModel to denoise the encoded image latents. controlnet (ControlNetModel or List[ControlNetModel]) — |
Provides additional conditioning to the unet during the denoising process. If you set multiple |
ControlNets as a list, the outputs from each ControlNet are added together to create one combined |
additional conditioning. 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. force_zeros_for_empty_prompt (bool, optional, defaults to "True") — |
Whether the negative prompt embeddings should always be set to 0. Also see the config of |
stabilityai/stable-diffusion-xl-base-1-0. add_watermarker (bool, optional) — |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.