DiT

译者：片刻小哥哥

项目地址：https://huggingface.apachecn.org/docs/diffusers/api/pipelines/dit

原始地址：https://huggingface.co/docs/diffusers/api/pipelines/dit

Scalable Diffusion Models with Transformers (DiT) is by William Peebles and Saining Xie.

The abstract from the paper is:

We explore a new class of diffusion models based on the transformer architecture. We train latent diffusion models of images, replacing the commonly-used U-Net backbone with a transformer that operates on latent patches. We analyze the scalability of our Diffusion Transformers (DiTs) through the lens of forward pass complexity as measured by Gflops. We find that DiTs with higher Gflops — through increased transformer depth/width or increased number of input tokens — consistently have lower FID. In addition to possessing good scalability properties, our largest DiT-XL/2 models outperform all prior diffusion models on the class-conditional ImageNet 512x512 and 256x256 benchmarks, achieving a state-of-the-art FID of 2.27 on the latter.

The original codebase can be found at facebookresearch/dit .

Make sure to check out the Schedulers guide to learn how to explore the tradeoff between scheduler speed and quality, and see the reuse components across pipelines section to learn how to efficiently load the same components into multiple pipelines.

DiTPipeline

class

diffusers.

DiTPipeline

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/dit/pipeline_dit.py#L31)

(

transformer

: Transformer2DModel

vae

: AutoencoderKL

scheduler

: KarrasDiffusionSchedulers

id2label

: typing.Union[typing.Dict[int, str], NoneType] = None

)

Parameters

• transformer ( Transformer2DModel ) — A class conditioned Transformer2DModel to denoise the encoded image latents.
• vae ( AutoencoderKL ) — Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations.
• scheduler ( DDIMScheduler ) — A scheduler to be used in combination with transformer to denoise the encoded image latents.

Pipeline for image generation based on a Transformer backbone instead of a UNet.

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.).

__call__

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/dit/pipeline_dit.py#L91)

(

class_labels

: typing.List[int]

guidance_scale

: float = 4.0

generator

: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None

num_inference_steps

: int = 50

output_type

: typing.Optional[str] = 'pil'

return_dict

: bool = True

)

→

export const metadata = 'undefined';

ImagePipelineOutput or tuple

Parameters

• class_labels (List[int]) — List of ImageNet class labels for the images to be generated.
• guidance_scale ( float , optional , defaults to 4.0) — 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 .
• generator ( torch.Generator , optional ) — A torch.Generator to make generation deterministic.
• num_inference_steps ( int , optional , defaults to 250) — The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference.
• 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.

Returns

export const metadata = 'undefined';

ImagePipelineOutput or tuple

export const metadata = 'undefined';

If return_dict is True , ImagePipelineOutput is returned, otherwise a tuple is returned where the first element is a list with the generated images

The call function to the pipeline for generation.

Examples:

>>> from diffusers import DiTPipeline, DPMSolverMultistepScheduler
>>> import torch

>>> pipe = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256", torch_dtype=torch.float16)
>>> pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
>>> pipe = pipe.to("cuda")

>>> # pick words from Imagenet class labels
>>> pipe.labels  # to print all available words

>>> # pick words that exist in ImageNet
>>> words = ["white shark", "umbrella"]

>>> class_ids = pipe.get_label_ids(words)

>>> generator = torch.manual_seed(33)
>>> output = pipe(class_labels=class_ids, num_inference_steps=25, generator=generator)

>>> image = output.images[0]  # label 'white shark'

get_label_ids

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/dit/pipeline_dit.py#L66)

(

label

: typing.Union[str, typing.List[str]]

)

→

export const metadata = 'undefined';

list of int

Parameters

• label ( str or dict of str ) — Label strings to be mapped to class ids.

Returns

export const metadata = 'undefined';

list of int

export const metadata = 'undefined';

Class ids to be processed by pipeline.

Map label strings from ImageNet to corresponding class ids.

ImagePipelineOutput

class

diffusers.

ImagePipelineOutput

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/pipelines/pipeline_utils.py#L110)

(

images

: typing.Union[typing.List[PIL.Image.Image], numpy.ndarray]

)

Parameters

• images ( List[PIL.Image.Image] or np.ndarray ) — 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.

---

我们一直在努力

apachecn/AiLearning

---