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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. | |
# Copyright 2022 The HuggingFace Team. All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
import inspect | |
from typing import List, Optional, Tuple, Union | |
import numpy as np | |
import paddle | |
import PIL | |
from ...models import UNet2DModel, VQModel | |
from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput | |
from ...schedulers import ( | |
DDIMScheduler, | |
DPMSolverMultistepScheduler, | |
EulerAncestralDiscreteScheduler, | |
EulerDiscreteScheduler, | |
LMSDiscreteScheduler, | |
PNDMScheduler, | |
) | |
from ...utils import PIL_INTERPOLATION | |
def preprocess(image): | |
w, h = image.size | |
w, h = map(lambda x: x - x % 32, (w, h)) # resize to integer multiple of 32 | |
image = image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]) | |
image = np.array(image).astype(np.float32) / 255.0 | |
image = image[None].transpose(0, 3, 1, 2) | |
image = paddle.to_tensor(image) | |
return 2.0 * image - 1.0 | |
class LDMSuperResolutionPipeline(DiffusionPipeline): | |
r""" | |
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 xxxx, etc.) | |
Parameters: | |
vqvae ([`VQModel`]): | |
Vector-quantized (VQ) VAE Model to encode and decode images to and from latent representations. | |
unet ([`UNet2DModel`]): U-Net architecture to denoise the encoded image. | |
scheduler ([`SchedulerMixin`]): | |
A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of | |
[`DDIMScheduler`], [`LMSDiscreteScheduler`], [`EulerDiscreteScheduler`], | |
[`EulerAncestralDiscreteScheduler`], [`DPMSolverMultistepScheduler`], or [`PNDMScheduler`]. | |
""" | |
def __init__( | |
self, | |
vqvae: VQModel, | |
unet: UNet2DModel, | |
scheduler: Union[ | |
DDIMScheduler, | |
PNDMScheduler, | |
LMSDiscreteScheduler, | |
EulerDiscreteScheduler, | |
EulerAncestralDiscreteScheduler, | |
DPMSolverMultistepScheduler, | |
], | |
): | |
super().__init__() | |
self.register_modules(vqvae=vqvae, unet=unet, scheduler=scheduler) | |
def __call__( | |
self, | |
image: Union[paddle.Tensor, PIL.Image.Image], | |
batch_size: Optional[int] = 1, | |
num_inference_steps: Optional[int] = 100, | |
eta: Optional[float] = 0.0, | |
generator: Optional[Union[paddle.Generator, List[paddle.Generator]]] = None, | |
output_type: Optional[str] = "pil", | |
return_dict: bool = True, | |
**kwargs, | |
) -> Union[Tuple, ImagePipelineOutput]: | |
r""" | |
Args: | |
image (`paddle.Tensor` or `PIL.Image.Image`): | |
`Image`, or tensor representing an image batch, that will be used as the starting point for the | |
process. | |
batch_size (`int`, *optional*, defaults to 1): | |
Number of images to generate. | |
num_inference_steps (`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. | |
eta (`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. | |
generator (`paddle.Generator`, *optional*): | |
One or a list of paddle generator(s) to make generation deterministic. | |
output_type (`str`, *optional*, defaults to `"pil"`): | |
The output format of the generate image. Choose between | |
[PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. | |
return_dict (`bool`, *optional*): | |
Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple. | |
Returns: | |
[`~pipeline_utils.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. | |
""" | |
if isinstance(image, PIL.Image.Image): | |
batch_size = 1 | |
elif isinstance(image, paddle.Tensor): | |
batch_size = image.shape[0] | |
else: | |
raise ValueError(f"`image` has to be of type `PIL.Image.Image` or `paddle.Tensor` but is {type(image)}") | |
if isinstance(image, PIL.Image.Image): | |
image = preprocess(image) | |
height, width = image.shape[-2:] | |
# in_channels should be 6: 3 for latents, 3 for low resolution image | |
latents_shape = (batch_size, self.unet.in_channels // 2, height, width) | |
latents_dtype = next(self.unet.named_parameters())[1].dtype | |
latents = paddle.randn(latents_shape, generator=generator, dtype=latents_dtype) | |
image = image.cast(latents_dtype) | |
self.scheduler.set_timesteps(num_inference_steps) | |
timesteps_tensor = self.scheduler.timesteps | |
# scale the initial noise by the standard deviation required by the scheduler | |
latents = latents * self.scheduler.init_noise_sigma | |
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature. | |
# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. | |
# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 | |
# and should be between [0, 1] | |
accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) | |
extra_kwargs = {} | |
if accepts_eta: | |
extra_kwargs["eta"] = eta | |
for t in self.progress_bar(timesteps_tensor): | |
# concat latents and low resolution image in the channel dimension. | |
latents_input = paddle.concat([latents, image], axis=1) | |
latents_input = self.scheduler.scale_model_input(latents_input, t) | |
# predict the noise residual | |
noise_pred = self.unet(latents_input, t).sample | |
# compute the previous noisy sample x_t -> x_t-1 | |
latents = self.scheduler.step(noise_pred, t, latents, **extra_kwargs).prev_sample | |
# decode the image latents with the VQVAE | |
image = self.vqvae.decode(latents).sample | |
image = paddle.clip(image, -1.0, 1.0) | |
image = image / 2 + 0.5 | |
image = image.transpose([0, 2, 3, 1]).cast("float32").numpy() | |
if output_type == "pil": | |
image = self.numpy_to_pil(image) | |
if not return_dict: | |
return (image,) | |
return ImagePipelineOutput(images=image) | |