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>>> 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_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. enable_freeu < source > ( s1: float s2: float b1: floa... |
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. StableDiffusionPipelineOutput class diffusers.pipelines.stable_diffusion.StableDiffusionPipelineOutput < source > ( images: Union nsfw_content_detected: Optional ) Parameters images (List[PIL.Image.Imag... |
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 Stable Diffusion pipelines. |
Unconditional Latent Diffusion |
Overview |
Unconditional Latent Diffusion was proposed in High-Resolution Image Synthesis with Latent Diffusion Models by Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer. |
The abstract of the paper is the following: |
By decomposing the image formation process into a sequential application of denoising autoencoders, diffusion models (DMs) achieve state-of-the-art synthesis results on image data and beyond. Additionally, their formulation allows for a guiding mechanism to control the image generation process without retraining. Howev... |
The original codebase can be found here. |
Tips: |
Available Pipelines: |
Pipeline |
Tasks |
Colab |
pipeline_latent_diffusion_uncond.py |
Unconditional Image Generation |
- |
Examples: |
LDMPipeline |
class diffusers.LDMPipeline |
< |
source |
> |
( |
vqvae: VQModel |
unet: UNet2DModel |
scheduler: DDIMScheduler |
) |
Parameters |
vqvae (VQModel) — |
Vector-quantized (VQ) Model to encode and decode images to and from latent representations. |
unet (UNet2DModel) — U-Net architecture to denoise the encoded image latents. |
scheduler (SchedulerMixin) — |
DDIMScheduler is to be used in combination with unet to denoise the encoded image latents. |
This model inherits from DiffusionPipeline. Check the superclass documentation for the generic methods the |
library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) |
__call__ |
< |
source |
> |
( |
batch_size: int = 1 |
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None |
eta: float = 0.0 |
num_inference_steps: int = 50 |
output_type: typing.Optional[str] = 'pil' |
return_dict: bool = True |
**kwargs |
) |
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