|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
from typing import List, Optional, Tuple, Union |
|
|
|
import torch |
|
|
|
from ....models import UNet2DModel |
|
from ....schedulers import ScoreSdeVeScheduler |
|
from ....utils.torch_utils import randn_tensor |
|
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput |
|
|
|
|
|
class ScoreSdeVePipeline(DiffusionPipeline): |
|
r""" |
|
Pipeline for unconditional image generation. |
|
|
|
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.). |
|
|
|
Parameters: |
|
unet ([`UNet2DModel`]): |
|
A `UNet2DModel` to denoise the encoded image. |
|
scheduler ([`ScoreSdeVeScheduler`]): |
|
A `ScoreSdeVeScheduler` to be used in combination with `unet` to denoise the encoded image. |
|
""" |
|
|
|
unet: UNet2DModel |
|
scheduler: ScoreSdeVeScheduler |
|
|
|
def __init__(self, unet: UNet2DModel, scheduler: ScoreSdeVeScheduler): |
|
super().__init__() |
|
self.register_modules(unet=unet, scheduler=scheduler) |
|
|
|
@torch.no_grad() |
|
def __call__( |
|
self, |
|
batch_size: int = 1, |
|
num_inference_steps: int = 2000, |
|
generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, |
|
output_type: Optional[str] = "pil", |
|
return_dict: bool = True, |
|
**kwargs, |
|
) -> Union[ImagePipelineOutput, Tuple]: |
|
r""" |
|
The call function to the pipeline for generation. |
|
|
|
Args: |
|
batch_size (`int`, *optional*, defaults to 1): |
|
The number of images to generate. |
|
generator (`torch.Generator`, `optional`): |
|
A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make |
|
generation deterministic. |
|
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: |
|
[`~pipelines.ImagePipelineOutput`] or `tuple`: |
|
If `return_dict` is `True`, [`~pipelines.ImagePipelineOutput`] is returned, otherwise a `tuple` is |
|
returned where the first element is a list with the generated images. |
|
""" |
|
|
|
img_size = self.unet.config.sample_size |
|
shape = (batch_size, 3, img_size, img_size) |
|
|
|
model = self.unet |
|
|
|
sample = randn_tensor(shape, generator=generator) * self.scheduler.init_noise_sigma |
|
sample = sample.to(self.device) |
|
|
|
self.scheduler.set_timesteps(num_inference_steps) |
|
self.scheduler.set_sigmas(num_inference_steps) |
|
|
|
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)): |
|
sigma_t = self.scheduler.sigmas[i] * torch.ones(shape[0], device=self.device) |
|
|
|
|
|
for _ in range(self.scheduler.config.correct_steps): |
|
model_output = self.unet(sample, sigma_t).sample |
|
sample = self.scheduler.step_correct(model_output, sample, generator=generator).prev_sample |
|
|
|
|
|
model_output = model(sample, sigma_t).sample |
|
output = self.scheduler.step_pred(model_output, t, sample, generator=generator) |
|
|
|
sample, sample_mean = output.prev_sample, output.prev_sample_mean |
|
|
|
sample = sample_mean.clamp(0, 1) |
|
sample = sample.cpu().permute(0, 2, 3, 1).numpy() |
|
if output_type == "pil": |
|
sample = self.numpy_to_pil(sample) |
|
|
|
if not return_dict: |
|
return (sample,) |
|
|
|
return ImagePipelineOutput(images=sample) |
|
|