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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. output_type (str, optional, defaults to "pil") — |
The output format of the generated image. Choose between PIL.Image or np.array. return_dict (bool, optional, defaults to True) — |
Whether or not to return a ImagePipelineOutput instead of a plain tuple. 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. cross_attention_kwargs (dict, optional) — |
A kwargs dictionary that if specified is passed along to the AttentionProcessor as defined in |
self.processor. noise_level (int, optional, defaults to 0) — |
The amount of noise to add to the image embeddings. A higher noise_level increases the variance in |
the final un-noised images. See StableUnCLIPPipeline.noise_image_embeddings() for more details. image_embeds (torch.FloatTensor, optional) — |
Pre-generated CLIP embeddings to condition the unet on. These latents are not used in the denoising |
process. If you want to provide pre-generated latents, pass them to __call__ as latents. 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. Returns |
ImagePipelineOutput or tuple |
~ pipeline_utils.ImagePipelineOutput if return_dict is True, otherwise a tuple. When returning |
a tuple, the first element is a list with the generated images. |
The call function to the pipeline for generation. Examples: Copied >>> import requests |
>>> import torch |
>>> from PIL import Image |
>>> from io import BytesIO |
>>> from diffusers import StableUnCLIPImg2ImgPipeline |
>>> pipe = StableUnCLIPImg2ImgPipeline.from_pretrained( |
... "fusing/stable-unclip-2-1-l-img2img", torch_dtype=torch.float16 |
... ) # TODO update model path |
>>> pipe = pipe.to("cuda") |
>>> url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/assets/stable-samples/img2img/sketch-mountains-input.jpg" |
>>> response = requests.get(url) |
>>> init_image = Image.open(BytesIO(response.content)).convert("RGB") |
>>> init_image = init_image.resize((768, 512)) |
>>> prompt = "A fantasy landscape, trending on artstation" |
>>> images = pipe(prompt, init_image).images |
>>> images[0].save("fantasy_landscape.png") enable_attention_slicing < source > ( slice_size: Union = 'auto' ) Parameters slice_size (str or int, optional, defaults to "auto") — |
When "auto", halves the input to the attention heads, so attention will be computed in two steps. If |
"max", maximum amount of memory will be saved by running only one slice at a time. If a number is |
provided, uses as many slices as attention_head_dim // slice_size. In this case, attention_head_dim |
must be a multiple of slice_size. Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor |
in slices to compute attention in several steps. For more than one attention head, the computation is performed |
sequentially over each head. This is useful to save some memory in exchange for a small speed decrease. ⚠️ Don’t enable attention slicing if you’re already using scaled_dot_product_attention (SDPA) from PyTorch |
2.0 or xFormers. These attention computations are already very memory efficient so you won’t need to enable |
this function. If you enable attention slicing with SDPA or xFormers, it can lead to serious slow downs! Examples: Copied >>> import torch |
>>> from diffusers import StableDiffusionPipeline |
>>> pipe = StableDiffusionPipeline.from_pretrained( |
... "runwayml/stable-diffusion-v1-5", |
... torch_dtype=torch.float16, |
... use_safetensors=True, |
... ) |
>>> prompt = "a photo of an astronaut riding a horse on mars" |
>>> pipe.enable_attention_slicing() |
>>> image = pipe(prompt).images[0] disable_attention_slicing < source > ( ) Disable sliced attention computation. If enable_attention_slicing was previously called, attention is |
computed 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. 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_xformers_memory_efficient_attention < source > ( attention_op: Optional = None ) Parameters attention_op (Callable, optional) — |
Override the default None operator for use as op argument to the |
memory_efficient_attention() |
function of xFormers. Enable memory efficient attention from xFormers. When this |
option is enabled, you should observe lower GPU memory usage and a potential speed up during inference. Speed |
up during training is not guaranteed. ⚠️ When memory efficient attention and sliced attention are both enabled, memory efficient attention takes |
precedent. Examples: Copied >>> import torch |
>>> from diffusers import DiffusionPipeline |
>>> from xformers.ops import MemoryEfficientAttentionFlashAttentionOp |
>>> pipe = DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2-1", torch_dtype=torch.float16) |
>>> pipe = pipe.to("cuda") |
>>> pipe.enable_xformers_memory_efficient_attention(attention_op=MemoryEfficientAttentionFlashAttentionOp) |
>>> # Workaround for not accepting attention shape using VAE for Flash Attention |
>>> pipe.vae.enable_xformers_memory_efficient_attention(attention_op=None) disable_xformers_memory_efficient_attention < source > ( ) Disable memory efficient attention from xFormers. encode_prompt < source > ( prompt device num_images_per_prompt do_classifier_free_guidance negative_prompt = None prompt_embeds: ... |
prompt to be encoded |
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). 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. 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. noise_image_embeddings < source > ( image_embeds: Tensor noise_level: int noise: Optional = None generator: Optional = None ) Add noise to the image embeddings. The amount of ... |
noise_level increases the variance in the final un-noised images. The noise is applied in two ways: A noise schedule is applied directly to the embeddings. A vector of sinusoidal time embeddings are appended to the output. In both cases, the amount of noise is controlled by the same noise_level. The embeddings are norm... |
List of denoised PIL images of length batch_size or NumPy array of shape (batch_size, height, width, num_channels). Output class for image pipelines. |
AudioLDM AudioLDM was proposed in AudioLDM: Text-to-Audio Generation with Latent Diffusion Models by Haohe Liu et al. Inspired by Stable Diffusion, AudioLDM |
is a text-to-audio latent diffusion model (LDM) that learns continuous audio representations from CLAP |
latents. AudioLDM takes a text prompt as input and predicts the corresponding audio. It can generate text-conditional |
sound effects, human speech and music. The abstract from the paper is: Text-to-audio (TTA) system has recently gained attention for its ability to synthesize general audio based on text descriptions. However, previous studies in TTA have limited generation quality with high computational costs. In this study, we propos... |
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