PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis is Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James Kwok, Ping Luo, Huchuan Lu, and Zhenguo Li.
The abstract from the paper is:
The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PIXART-α, a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image generators (e.g., Imagen, SDXL, and even Midjourney), reaching near-commercial application standards. Additionally, it supports high-resolution image synthesis up to 1024px resolution with low training cost, as shown in Figure 1 and 2. To achieve this goal, three core designs are proposed: (1) Training strategy decomposition: We devise three distinct training steps that separately optimize pixel dependency, text-image alignment, and image aesthetic quality; (2) Efficient T2I Transformer: We incorporate cross-attention modules into Diffusion Transformer (DiT) to inject text conditions and streamline the computation-intensive class-condition branch; (3) High-informative data: We emphasize the significance of concept density in text-image pairs and leverage a large Vision-Language model to auto-label dense pseudo-captions to assist text-image alignment learning. As a result, PIXART-α’s training speed markedly surpasses existing large-scale T2I models, e.g., PIXART-α only takes 10.8% of Stable Diffusion v1.5’s training time (675 vs. 6,250 A100 GPU days), saving nearly $300,000 ($26,000 vs. $320,000) and reducing 90% CO2 emissions. Moreover, compared with a larger SOTA model, RAPHAEL, our training cost is merely 1%. Extensive experiments demonstrate that PIXART-α excels in image quality, artistry, and semantic control. We hope PIXART-α will provide new insights to the AIGC community and startups to accelerate building their own high-quality yet low-cost generative models from scratch.
You can find the original codebase at PixArt-alpha/PixArt-alpha and all the available checkpoints at PixArt-alpha.
Some notes about this pipeline:
( tokenizer: T5Tokenizer text_encoder: T5EncoderModel vae: AutoencoderKL transformer: Transformer2DModel scheduler: DPMSolverMultistepScheduler )
Parameters
T5EncoderModel
) —
Frozen text-encoder. PixArt-Alpha uses
T5, specifically the
t5-v1_1-xxl variant. T5Tokenizer
) —
Tokenizer of class
T5Tokenizer. Transformer2DModel
to denoise the encoded image latents. transformer
to denoise the encoded image latents. Pipeline for text-to-image generation using PixArt-Alpha.
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.)
( prompt: typing.Union[str, typing.List[str]] = None negative_prompt: str = '' num_inference_steps: int = 20 timesteps: typing.List[int] = None guidance_scale: float = 4.5 num_images_per_prompt: typing.Optional[int] = 1 height: typing.Optional[int] = None width: typing.Optional[int] = None eta: float = 0.0 generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None latents: typing.Optional[torch.FloatTensor] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None prompt_attention_mask: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_attention_mask: typing.Optional[torch.FloatTensor] = None output_type: typing.Optional[str] = 'pil' return_dict: bool = True callback: typing.Union[typing.Callable[[int, int, torch.FloatTensor], NoneType], NoneType] = None callback_steps: int = 1 clean_caption: bool = True use_resolution_binning: bool = True **kwargs ) → ImagePipelineOutput or tuple
Parameters
str
or List[str]
, optional) —
The prompt or prompts to guide the image generation. If not defined, one has to pass prompt_embeds
.
instead. 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
). int
, optional, defaults to 100) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference. List[int]
, optional) —
Custom timesteps to use for the denoising process. If not defined, equal spaced num_inference_steps
timesteps are used. Must be in descending order. float
, optional, defaults to 7.0) —
Guidance scale as defined in Classifier-Free Diffusion Guidance.
guidance_scale
is defined as w
of equation 2. of Imagen
Paper. Guidance scale is enabled by setting guidance_scale > 1
. Higher guidance scale encourages to generate images that are closely linked to the text prompt
,
usually at the expense of lower image quality. int
, optional, defaults to 1) —
The number of images to generate per prompt. int
, optional, defaults to self.unet.config.sample_size) —
The height in pixels of the generated image. int
, optional, defaults to self.unet.config.sample_size) —
The width in pixels of the generated image. float
, optional, defaults to 0.0) —
Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to
schedulers.DDIMScheduler, will be ignored for others. torch.Generator
or List[torch.Generator]
, optional) —
One or a list of torch generator(s)
to make generation deterministic. torch.FloatTensor
, optional) —
Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
tensor will ge generated by sampling using the supplied random generator
. 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. torch.FloatTensor
, optional) — Pre-generated attention mask for text embeddings. torch.FloatTensor
, optional) —
Pre-generated negative text embeddings. For PixArt-Alpha this negative prompt should be "". If not
provided, negative_prompt_embeds will be generated from negative_prompt
input argument. torch.FloatTensor
, optional) —
Pre-generated attention mask for negative text embeddings. str
, optional, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
. bool
, optional, defaults to True
) —
Whether or not to return a ~pipelines.stable_diffusion.IFPipelineOutput
instead of a plain tuple. 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)
. 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. bool
, optional, defaults to True
) —
Whether or not to clean the caption before creating embeddings. Requires beautifulsoup4
and ftfy
to
be installed. If the dependencies are not installed, the embeddings will be created from the raw
prompt. bool
defaults to True
) —
If set to True
, the requested height and width are first mapped to the closest resolutions using
ASPECT_RATIO_1024_BIN
. After the produced latents are decoded into images, they are resized back to
the requested resolution. Useful for generating non-square images. Returns
ImagePipelineOutput or tuple
If return_dict
is True
, ImagePipelineOutput is returned, otherwise a tuple
is
returned where the first element is a list with the generated images
Function invoked when calling the pipeline for generation.
Examples:
>>> import torch
>>> from diffusers import PixArtAlphaPipeline
>>> # You can replace the checkpoint id with "PixArt-alpha/PixArt-XL-2-512x512" too.
>>> pipe = PixArtAlphaPipeline.from_pretrained("PixArt-alpha/PixArt-XL-2-1024-MS", torch_dtype=torch.float16)
>>> # Enable memory optimizations.
>>> pipe.enable_model_cpu_offload()
>>> prompt = "A small cactus with a happy face in the Sahara desert."
>>> image = pipe(prompt).images[0]
Returns binned height and width.
( prompt: typing.Union[str, typing.List[str]] do_classifier_free_guidance: bool = True negative_prompt: str = '' num_images_per_prompt: int = 1 device: typing.Optional[torch.device] = None prompt_embeds: typing.Optional[torch.FloatTensor] = None negative_prompt_embeds: typing.Optional[torch.FloatTensor] = None prompt_attention_mask: typing.Optional[torch.FloatTensor] = None negative_prompt_attention_mask: typing.Optional[torch.FloatTensor] = None clean_caption: bool = False **kwargs )
Parameters
str
or List[str]
, optional) —
prompt to be encoded str
or List[str]
, optional) —
The prompt 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
). For
PixArt-Alpha, this should be "". bool
, optional, defaults to True
) —
whether to use classifier free guidance or not int
, optional, defaults to 1) —
number of images that should be generated per prompt
device — (torch.device
, optional):
torch device to place the resulting embeddings on 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. torch.FloatTensor
, optional) —
Pre-generated negative text embeddings. For PixArt-Alpha, it’s should be the embeddings of the ""
string. False
) —
If True
, the function will preprocess and clean the provided caption before encoding. Encodes the prompt into text encoder hidden states.