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as the target_size for most cases. Part of SDXL’s micro-conditioning as explained in section 2.2 of |
https://huggingface.co/papers/2307.01952. For more |
information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. adapter_conditioning_scale (float or List[float], optional, defaults to 1.0) — |
The outputs of the adapter are multiplied by adapter_conditioning_scale before they are added to the |
residual in the original unet. If multiple adapters are specified in init, you can set the |
corresponding scale as a list. adapter_conditioning_factor (float, optional, defaults to 1.0) — |
The fraction of timesteps for which adapter should be applied. If adapter_conditioning_factor is |
0.0, adapter is not applied at all. If adapter_conditioning_factor is 1.0, adapter is applied for |
all timesteps. If adapter_conditioning_factor is 0.5, adapter is applied for half of the timesteps. 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 |
~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput or tuple |
~pipelines.stable_diffusion.StableDiffusionAdapterPipelineOutput 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 T2IAdapter, StableDiffusionXLAdapterPipeline, DDPMScheduler |
>>> from diffusers.utils import load_image |
>>> sketch_image = load_image("https://huggingface.co/Adapter/t2iadapter/resolve/main/sketch.png").convert("L") |
>>> model_id = "stabilityai/stable-diffusion-xl-base-1.0" |
>>> adapter = T2IAdapter.from_pretrained( |
... "Adapter/t2iadapter", |
... subfolder="sketch_sdxl_1.0", |
... torch_dtype=torch.float16, |
... adapter_type="full_adapter_xl", |
... ) |
>>> scheduler = DDPMScheduler.from_pretrained(model_id, subfolder="scheduler") |
>>> pipe = StableDiffusionXLAdapterPipeline.from_pretrained( |
... model_id, adapter=adapter, torch_dtype=torch.float16, variant="fp16", scheduler=scheduler |
... ).to("cuda") |
>>> generator = torch.manual_seed(42) |
>>> sketch_image_out = pipe( |
... prompt="a photo of a dog in real world, high quality", |
... negative_prompt="extra digit, fewer digits, cropped, worst quality, low quality", |
... image=sketch_image, |
... generator=generator, |
... guidance_scale=7.5, |
... ).images[0] 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. disable_freeu < source > ( ) Disables the FreeU mechanism if enabled. disable_vae_tiling < source > ( ) Disable tiled VAE... |
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_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 for saving a large amount of memory and to allow |
processing 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_2: Optional = None prompt_embeds: Optional = None negative_prompt_embeds: 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 |
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