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 warnings

import torch

from tqdm.auto 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)

    @torch.no_grad()
    def __call__(self, batch_size=1, generator=None, eta=0.0, num_inference_steps=50, output_type="pil", **kwargs):

        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)

        # eta corresponds to η in paper and should be between [0, 1]

        # 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)

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

        for t in tqdm(self.scheduler.timesteps):
            # 1. predict noise model_output
            model_output = self.unet(image, t)["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(model_output, t, image, eta)["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}