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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. 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.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.models.attention_processor. guidance_rescale (float, optional, defaults to 0.0) — |
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 (height, width) 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 (height, width). Part of SDXL’s micro-conditioning as explained in |
section 2.2 of https://huggingface.co/papers/2307.01952. aesthetic_score (float, optional, defaults to 6.0) — |
Used to simulate an aesthetic score of the generated image by influencing the positive text condition. |
Part of SDXL’s micro-conditioning as explained in section 2.2 of |
https://huggingface.co/papers/2307.01952. negative_aesthetic_score (float, optional, defaults to 2.5) — |
Part of SDXL’s micro-conditioning as explained in section 2.2 of |
https://huggingface.co/papers/2307.01952. Can be used to |
simulate an aesthetic score of the generated image by influencing the negative text condition. Returns |
~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput or tuple |
~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput if return_dict is True, otherwise a |
tuple. When returning a tuple, the first element is a list with the generated images. |
Function invoked when calling the pipeline for generation. Examples: Copied >>> import torch |
>>> from diffusers import StableDiffusionXLInstructPix2PixPipeline |
>>> from diffusers.utils import load_image |
>>> resolution = 768 |
>>> image = load_image( |
... "https://hf.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png" |
... ).resize((resolution, resolution)) |
>>> edit_instruction = "Turn sky into a cloudy one" |
>>> pipe = StableDiffusionXLInstructPix2PixPipeline.from_pretrained( |
... "diffusers/sdxl-instructpix2pix-768", torch_dtype=torch.float16 |
... ).to("cuda") |
>>> edited_image = pipe( |
... prompt=edit_instruction, |
... image=image, |
... height=resolution, |
... width=resolution, |
... guidance_scale=3.0, |
... image_guidance_scale=1.5, |
... num_inference_steps=30, |
... ).images[0] |
>>> edited_image disable_freeu < source > ( ) Disables the FreeU mechanism if enabled. disable_vae_slicing < source > ( ) Disable sliced VAE decoding. If enable_vae_slicing was previously invoked, 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 invoked, 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 to save a large amount of memory and to allow the processing of larger images. 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... |
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. Encodes the prompt into text encoder hidden states. |
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