# 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 warnings

import torch

from ...pipeline_utils import DiffusionPipeline

class PNDMPipeline(DiffusionPipeline):

def __init__(self, unet, scheduler):

super().__init__()

scheduler = scheduler.set_format("pt")

self.register_modules(unet=unet, scheduler=scheduler)

@torch.no_grad()

def __call__(self, batch_size=1, generator=None, num_inference_steps=50, output_type="pil", **kwargs):

# For more information on the sampling method you can take a look at Algorithm 2 of

# the official paper: https://arxiv.org/pdf/2202.09778.pdf

if "torch_device" in kwargs:

device = kwargs.pop("torch_device")

warnings.warn(

"`torch_device` is deprecated as an input argument to `__call__` and will be removed in v0.3.0."

" Consider using `pipe.to(torch_device)` instead."

)

# Set device as before (to be removed in 0.3.0)

if device is None:

device = "cuda" if torch.cuda.is_available() else "cpu"

self.to(device)

# Sample gaussian noise to begin loop

image = torch.randn(

(batch_size, self.unet.in_channels, self.unet.sample_size, self.unet.sample_size),

generator=generator,

)

image = image.to(self.device)

self.scheduler.set_timesteps(num_inference_steps)

for t in self.progress_bar(self.scheduler.timesteps):

model_output = self.unet(image, t)["sample"]

image = self.scheduler.step(model_output, t, image)["prev_sample"]

image = (image / 2 + 0.5).clamp(0, 1)

image = image.cpu().permute(0, 2, 3, 1).numpy()

if output_type == "pil":

image = self.numpy_to_pil(image)

return {"sample": image}