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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 Parameters 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. |
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: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: Optional = None pooled_prompt_embeds: Optional = None negative_pooled_prompt_embeds: Optional = None lora_scale: Optional = None clip_skip: Optional = None ) Parameters prompt (str or List[str], optional) — |
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 images ([List[PIL.Image.Image], np.ndarray]) — |
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 provide a depth map, the ControlNet model generates an image that’ll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process. The abstract from the paper is: We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected with “zero convolutions” (zero-initialized convolution layers) that progressively grow the parameters from zero and ensure that no harmful noise could affect the finetuning. We test various conditioning controls, eg, edges, depth, segmentation, human pose, etc, with Stable Diffusion, using single or multiple conditions, with or without prompts. We show that the training of ControlNets is robust with small (<50k) and large (>1m) datasets. Extensive results show that ControlNet may facilitate wider applications to control image diffusion models. You can find additional smaller Stable Diffusion XL (SDXL) ControlNet checkpoints from the 🤗 Diffusers Hub organization, and browse community-trained checkpoints on the Hub. 🧪 Many of the SDXL ControlNet checkpoints are experimental, and there is a lot of room for improvement. Feel free to open an Issue and leave us feedback on how we can improve! If you don’t see a checkpoint you’re interested in, you can train your own SDXL ControlNet with our training script. 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. StableDiffusionXLControlNetPipeline class diffusers.StableDiffusionXLControlNetPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel text_encoder_2: CLIPTextModelWithProjection tokenizer: CLIPTokenizer tokenizer_2: CLIPTokenizer unet: UNet2DConditionModel controlnet: Union scheduler: KarrasDiffusionSchedulers force_zeros_for_empty_prompt: bool = True add_watermarker: Optional = None 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). 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) — |
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