pipeline_ddim.py

# 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 torch

import tqdm

from ...pipeline_utils import DiffusionPipeline


class DDIMPipeline(DiffusionPipeline):
    def __init__(self, unet, scheduler):
        super().__init__()
        scheduler = scheduler.set_format("pt")
        self.register_modules(unet=unet, scheduler=scheduler)

    def __call__(self, batch_size=1, generator=None, torch_device=None, eta=0.0, num_inference_steps=50):
        # eta corresponds to η in paper and should be between [0, 1]
        if torch_device is None:
            torch_device = "cuda" if torch.cuda.is_available() else "cpu"

        self.unet.to(torch_device)

        # Sample gaussian noise to begin loop
        image = torch.randn(
            (batch_size, self.unet.in_channels, self.unet.resolution, self.unet.resolution),
            generator=generator,
        )
        image = image.to(torch_device)

        # set step values
        self.scheduler.set_timesteps(num_inference_steps)

        for t in tqdm.tqdm(self.scheduler.timesteps):
            # 1. predict noise residual
            with torch.no_grad():
                residual = self.unet(image, t)

                if isinstance(residual, dict):
                    residual = residual["sample"]

            # 2. predict previous mean of image x_t-1 and add variance depending on eta
            # do x_t -> x_t-1
            image = self.scheduler.step(residual, t, image, eta)["prev_sample"]

        return {"sample": image}