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. However, since these models typically operate directly in pixel space, optimization of powerful DMs often consumes hundreds of GPU days and inference is expensive due to sequential evaluations. To enable DM training on limited computational resources while retaining their quality and flexibility, we apply them in the latent space of powerful pretrained autoencoders. In contrast to previous work, training diffusion models on such a representation allows for the first time to reach a near-optimal point between complexity reduction and detail preservation, greatly boosting visual fidelity. By introducing cross-attention layers into the model architecture, we turn diffusion models into powerful and flexible generators for general conditioning inputs such as text or bounding boxes and high-resolution synthesis becomes possible in a convolutional manner. Our latent diffusion models (LDMs) achieve a new state of the art for image inpainting and highly competitive performance on various tasks, including unconditional image generation, semantic scene synthesis, and super-resolution, while significantly reducing computational requirements compared to pixel-based DMs.
The original codebase can be found here.
Pipeline | Tasks | Colab |
---|---|---|
pipeline_latent_diffusion.py | Text-to-Image Generation | - |
pipeline_latent_diffusion_superresolution.py | Super Resolution | - |
( vqvae: typing.Union[diffusers.models.vq_model.VQModel, diffusers.models.autoencoder_kl.AutoencoderKL] bert: PreTrainedModel tokenizer: PreTrainedTokenizer unet: typing.Union[diffusers.models.unet_2d.UNet2DModel, diffusers.models.unet_2d_condition.UNet2DConditionModel] scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler] )
Parameters
LDMBertModel
) —
Text-encoder model based on BERT architecture.
transformers.BertTokenizer
) —
Tokenizer of class
BertTokenizer.
unet
to denoise the encoded image latents. Can be one of
DDIMScheduler, LMSDiscreteScheduler, or PNDMScheduler.
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]]
height: typing.Optional[int] = None
width: typing.Optional[int] = None
num_inference_steps: typing.Optional[int] = 50
guidance_scale: typing.Optional[float] = 1.0
eta: typing.Optional[float] = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
latents: typing.Optional[torch.FloatTensor] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
**kwargs
)
→
ImagePipelineOutput or tuple
Parameters
str
or List[str]
) —
The prompt or prompts to guide the image generation.
int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The height in pixels of the generated image.
int
, optional, defaults to self.unet.config.sample_size * self.vae_scale_factor) —
The width in pixels of the generated image.
int
, optional, defaults to 50) —
The number of denoising steps. More denoising steps usually lead to a higher quality image at the
expense of slower inference.
float
, optional, defaults to 1.0) —
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
at
the, usually at the expense of lower image quality.
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
.
str
, optional, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
.
bool
, optional) —
Whether or not to return a ImagePipelineOutput instead of a plain tuple.
Returns
ImagePipelineOutput or tuple
~pipelines.utils.ImagePipelineOutput
if return_dict
is
True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.
( vqvae: VQModel unet: UNet2DModel scheduler: typing.Union[diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler] )
Parameters
unet
to denoise the encoded image latens. Can be one of
DDIMScheduler, LMSDiscreteScheduler, EulerDiscreteScheduler,
EulerAncestralDiscreteScheduler, DPMSolverMultistepScheduler, or PNDMScheduler.
A pipeline for image super-resolution using Latent
This class 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.)
(
image: typing.Union[torch.Tensor, PIL.Image.Image] = None
batch_size: typing.Optional[int] = 1
num_inference_steps: typing.Optional[int] = 100
eta: typing.Optional[float] = 0.0
generator: typing.Union[torch._C.Generator, typing.List[torch._C.Generator], NoneType] = None
output_type: typing.Optional[str] = 'pil'
return_dict: bool = True
)
→
ImagePipelineOutput or tuple
Parameters
torch.Tensor
or PIL.Image.Image
) —
Image
, or tensor representing an image batch, that will be used as the starting point for the
process.
int
, optional, defaults to 1) —
Number of images to generate.
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.
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
, optional) —
One or a list of torch generator(s)
to make generation deterministic.
str
, optional, defaults to "pil"
) —
The output format of the generate image. Choose between
PIL: PIL.Image.Image
or np.array
.
bool
, optional) —
Whether or not to return a ImagePipelineOutput instead of a plain tuple.
Returns
ImagePipelineOutput or tuple
~pipelines.utils.ImagePipelineOutput
if return_dict
is
True, otherwise a `tuple. When returning a tuple, the first element is a list with the generated images.