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A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined under |
self.processor in |
diffusers.models.attention_processor. circular_padding (bool, optional, defaults to False) β |
If set to True, circular padding is applied to ensure there are no stitching artifacts. Circular |
padding allows the model to seamlessly generate a transition from the rightmost part of the image to |
the leftmost part, maintaining consistency in a 360-degree sense. 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. Returns |
StableDiffusionPipelineOutput or tuple |
If return_dict is True, StableDiffusionPipelineOutput is returned, |
otherwise a tuple is returned where the first element is a list with the generated images and the |
second element is a list of bools indicating whether the corresponding generated image contains |
βnot-safe-for-workβ (nsfw) content. |
The call function to the pipeline for generation. Examples: Copied >>> import torch |
>>> from diffusers import StableDiffusionPanoramaPipeline, DDIMScheduler |
>>> model_ckpt = "stabilityai/stable-diffusion-2-base" |
>>> scheduler = DDIMScheduler.from_pretrained(model_ckpt, subfolder="scheduler") |
>>> pipe = StableDiffusionPanoramaPipeline.from_pretrained( |
... model_ckpt, scheduler=scheduler, torch_dtype=torch.float16 |
... ) |
>>> pipe = pipe.to("cuda") |
>>> prompt = "a photo of the dolomites" |
>>> image = pipe(prompt).images[0] 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 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. StableDiffusionPipelineOutput class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput < source > ( images: Union nsfw_content_detected: Optional ) Parameters images (List[PIL.Image.Image] or np.ndarray) β |
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]) β |
List indicating whether the corresponding generated image contains βnot-safe-for-workβ (nsfw) content or |
None if safety checking could not be performed. Output class for Stable Diffusion pipelines. |
Image-to-image The Stable Diffusion model can also be applied to image-to-image generation by passing a text prompt and an initial image to condition the generation of new images. The StableDiffusionImg2ImgPipeline uses the diffusion-denoising mechanism proposed in SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations by Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, Stefano Ermon. The abstract from the paper is: Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user input (e.g., hand-drawn colored strokes) and realism of the synthesized image. Existing GAN-based methods attempt to achieve such balance using either conditional GANs or GAN inversions, which are challenging and often require additional training data or loss functions for individual applications. To address these issues, we introduce a new image synthesis and editing method, Stochastic Differential Editing (SDEdit), based on a diffusion model generative prior, which synthesizes realistic images by iteratively denoising through a stochastic differential equation (SDE). Given an input image with user guide of any type, SDEdit first adds noise to the input, then subsequently denoises the resulting image through the SDE prior to increase its realism. SDEdit does not require task-specific training or inversions and can naturally achieve the balance between realism and faithfulness. SDEdit significantly outperforms state-of-the-art GAN-based methods by up to 98.09% on realism and 91.72% on overall satisfaction scores, according to a human perception study, on multiple tasks, including stroke-based image synthesis and editing as well as image compositing. Make sure to check out the Stable Diffusion Tips section to learn how to explore the tradeoff between scheduler speed and quality, and how to reuse pipeline components efficiently! StableDiffusionImg2ImgPipeline class diffusers.StableDiffusionImg2ImgPipeline < source > ( vae: AutoencoderKL text_encoder: CLIPTextModel tokenizer: CLIPTokenizer unet: UNet2DConditionModel scheduler: KarrasDiffusionSchedulers safety_checker: StableDiffusionSafetyChecker feature_extractor: CLIPImageProcessor image_encoder: CLIPVisionModelWithProjection = None requires_safety_checker: bool = True ) 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 (clip-vit-large-patch14). tokenizer (CLIPTokenizer) β |
A CLIPTokenizer to tokenize text. unet (UNet2DConditionModel) β |
A UNet2DConditionModel 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. safety_checker (StableDiffusionSafetyChecker) β |
Classification module that estimates whether generated images could be considered offensive or harmful. |
Please refer to the model card for more details |
about a modelβs potential harms. feature_extractor (CLIPImageProcessor) β |
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 from_single_file() for loading .ckpt files load_ip_adapter() for loading IP Adapters __call__ < source > ( prompt: Union = None image: Union = None strength: float = 0.8 num_inference_steps: Optional = 50 timesteps: List = None guidance_scale: Optional = 7.5 negative_prompt: Union = None num_images_per_prompt: Optional = 1 eta: Optional = 0.0 generator: Union = 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: int = None callback_on_step_end: Optional = None callback_on_step_end_tensor_inputs: List = ['latents'] **kwargs ) β StableDiffusionPipelineOutput or tuple Parameters prompt (str or List[str], optional) β |
The prompt or prompts to guide image generation. If not defined, you need to pass prompt_embeds. image (torch.FloatTensor, PIL.Image.Image, np.ndarray, List[torch.FloatTensor], List[PIL.Image.Image], or List[np.ndarray]) β |
Image, numpy array or tensor representing an image batch to be used as the starting point. For both |
numpy array and pytorch tensor, the expected value range is between [0, 1] If itβs a tensor or a list |
or tensors, the expected shape should be (B, C, H, W) or (C, H, W). If it is a numpy array or a |
list of arrays, the expected shape should be (B, H, W, C) or (H, W, C) It can also accept image |
latents as image, but if passing latents directly it is not encoded again. strength (float, optional, defaults to 0.8) β |
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) β |
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. timesteps (List[int], optional) β |
Custom timesteps to use for the denoising process with schedulers which support a timesteps argument |
in their set_timesteps method. If not defined, the default behavior when num_inference_steps is |
passed will be used. Must be in descending order. guidance_scale (float, optional, defaults to 7.5) β |
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) β |
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). num_images_per_prompt (int, optional, defaults to 1) β |
The number of images to generate per prompt. eta (float, optional, defaults to 0.0) β |
Corresponds to parameter eta (Ξ·) from the DDIM paper. Only applies |
to the DDIMScheduler, and is ignored in other schedulers. generator (torch.Generator or List[torch.Generator], optional) β |
A torch.Generator to make |
generation deterministic. prompt_embeds (torch.FloatTensor, optional) β |
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) β |
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 β (PipelineImageInput, optional): Optional image input to work with IP Adapters. output_type (str, optional, defaults to "pil") β |
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) β |
Whether or not to return a StableDiffusionPipelineOutput instead of a |
plain tuple. cross_attention_kwargs (dict, optional) β |
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in |
self.processor. 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. callback_on_step_end (Callable, optional) β |
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) β |
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