inpaint_pipeline.py

# 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
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# 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
from typing import Optional, Union, List, Callable
import PIL
import numpy as np

from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_inpaint_legacy import preprocess_image, deprecate, StableDiffusionInpaintPipelineLegacy, StableDiffusionPipelineOutput, PIL_INTERPOLATION

def preprocess_mask(mask, scale_factor=8):
    mask = mask.convert("L")
    w, h = mask.size
    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32

    #input_mask = mask.resize((w, h), resample=PIL_INTERPOLATION["nearest"])
    input_mask = np.array(mask).astype(np.float32) / 255.0
    input_mask = np.tile(input_mask, (3, 1, 1))
    input_mask = input_mask[None].transpose(0, 1, 2, 3)  # add batch dimension
    input_mask = 1 - input_mask  # repaint white, keep black
    input_mask = torch.round(torch.from_numpy(input_mask))

    mask = mask.resize((w // scale_factor, h // scale_factor), resample=PIL_INTERPOLATION["nearest"])
    mask = np.array(mask).astype(np.float32) / 255.0
    mask = np.tile(mask, (4, 1, 1))
    mask = mask[None].transpose(0, 1, 2, 3)  # add batch dimension
    mask = 1 - mask  # repaint white, keep black
    mask = torch.round(torch.from_numpy(mask))

    return mask, input_mask



class SDInpaintPipeline(StableDiffusionInpaintPipelineLegacy):

    # forward call is same as StableDiffusionInpaintPipelineLegacy, but with line added to avoid noise added to final latents right before decoding step
    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        mask_image: Union[torch.FloatTensor, PIL.Image.Image] = None,
        strength: float = 0.8,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        add_predicted_noise: Optional[bool] = False,
        eta: Optional[float] = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        preserve_unmasked_image: bool = True,
        **kwargs,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            image (`torch.FloatTensor` or `PIL.Image.Image`):
                `Image`, or tensor representing an image batch, that will be used as the starting point for the
                process. This is the image whose masked region will be inpainted.
            mask_image (`torch.FloatTensor` or `PIL.Image.Image`):
                `Image`, or tensor representing an image batch, to mask `image`. White pixels in the mask will be
                replaced by noise and therefore repainted, while black pixels will be preserved. If `mask_image` is a
                PIL image, it will be converted to a single channel (luminance) before use. If it's a tensor, it should
                contain one color channel (L) instead of 3, so the expected shape would be `(B, H, W, 1)`.
            strength (`float`, *optional*, defaults to 0.8):
                Conceptually, indicates how much to inpaint the masked area. Must be between 0 and 1. When `strength`
                is 1, the denoising process will be run on the masked area for the full number of iterations specified
                in `num_inference_steps`. `image` will be used as a reference for the masked area, adding more noise to
                that region the larger the `strength`. If `strength` is 0, no inpainting will occur.
            num_inference_steps (`int`, *optional*, defaults to 50):
                The reference number of denoising steps. More denoising steps usually lead to a higher quality image at
                the expense of slower inference. This parameter will be modulated by `strength`, as explained above.
            guidance_scale (`float`, *optional*, defaults to 7.5):
                Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598).
                `guidance_scale` is defined as `w` of equation 2. of [Imagen
                Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale >
                1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`,
                usually at the expense of lower image quality.
            negative_prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts not to guide the image generation. Ignored when not using guidance (i.e., ignored
                if `guidance_scale` is less than `1`).
            num_images_per_prompt (`int`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            add_predicted_noise (`bool`, *optional*, defaults to True):
                Use predicted noise instead of random noise when constructing noisy versions of the original image in
                the reverse diffusion process
            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 (`torch.Generator`, *optional*):
                One or a list of [torch generator(s)](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 generate image. Choose between
                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
            return_dict (`bool`, *optional*, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                plain tuple.
            callback (`Callable`, *optional*):
                A function that will be called every `callback_steps` steps during inference. The function will be
                called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`.
            callback_steps (`int`, *optional*, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            preserve_unmasked_image (`bool`, *optional*, defaults to `True`):
                Whether or not to preserve the unmasked portions of the original image in the inpainted output. If False, 
                inpainting of the masked latents may produce noticeable distortion of unmasked portions of the decoded 
                image.

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] if `return_dict` is True, otherwise a `tuple.
            When returning a tuple, the first element is a list with the generated images, and the second element is a
            list of `bool`s denoting whether the corresponding generated image likely represents "not-safe-for-work"
            (nsfw) content, according to the `safety_checker`.
        """
        message = "Please use `image` instead of `init_image`."
        init_image = deprecate("init_image", "0.13.0", message, take_from=kwargs)
        image = init_image or image

        # 1. Check inputs
        self.check_inputs(prompt, strength, callback_steps)

        # 2. Define call parameters
        batch_size = 1 if isinstance(prompt, str) else len(prompt)
        device = self._execution_device
        # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
        # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
        # corresponds to doing no classifier free guidance.
        do_classifier_free_guidance = guidance_scale > 1.0

        # 3. Encode input prompt
        text_embeddings = self._encode_prompt(
            prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt
        )

        # 4. Preprocess image and mask
        if not isinstance(image, torch.FloatTensor):
            image = preprocess_image(image)

        # get mask corresponding to input latents as well as image
        if not isinstance(mask_image, torch.FloatTensor):
            mask_image, input_mask_image = preprocess_mask(mask_image, self.vae_scale_factor)

        # 5. set timesteps
        self.scheduler.set_timesteps(num_inference_steps, device=device)
        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength, device)
        latent_timestep = timesteps[:1].repeat(batch_size * num_images_per_prompt)

        # 6. Prepare latent variables
        # encode the init image into latents and scale the latents
        latents, init_latents_orig, noise = self.prepare_latents(
            image, latent_timestep, batch_size, num_images_per_prompt, text_embeddings.dtype, device, generator
        )

        # 7. Prepare mask latent
        mask = mask_image.to(device=self.device, dtype=latents.dtype)
        mask = torch.cat([mask] * batch_size * num_images_per_prompt)

        # 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
        extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta)

        # 9. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        with self.progress_bar(total=num_inference_steps) as progress_bar:
            for i, t in enumerate(timesteps):
                
                # expand the latents if we are doing classifier free guidance
                latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
                latent_model_input = self.scheduler.scale_model_input(latent_model_input, t)

                # predict the noise residual
                noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=text_embeddings).sample

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
                    noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

                # compute the previous noisy sample x_t -> x_t-1
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample
                # masking
                if add_predicted_noise:
                    init_latents_proper = self.scheduler.add_noise(
                        init_latents_orig, noise_pred_uncond, torch.tensor([t])
                    )
                else:
                    init_latents_proper = self.scheduler.add_noise(init_latents_orig, noise, torch.tensor([t]))

                latents = (init_latents_proper * mask) + (latents * (1 - mask))

                # call the callback, if provided
                if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents)

        # use original latents corresponding to unmasked portions of the image
        # necessary step because noise is still added to "init_latents_proper" after final denoising step
        latents = (init_latents_orig * mask) + (latents * (1 - mask))

        # 10. Post-processing
        if preserve_unmasked_image:
            # decode latents
            latents = 1 / 0.18215 * latents
            inpaint_image = self.vae.decode(latents).sample

            # restore unmasked parts of image with original image
            input_mask_image = input_mask_image.to(inpaint_image)
            image = image.to(inpaint_image)
            image = (image * input_mask_image) + (inpaint_image * (1 - input_mask_image)) # use original unmasked portions of image to avoid degradation

            # post-processing of image
            image = (image / 2 + 0.5).clamp(0, 1)
            # we always cast to float32 as this does not cause significant overhead and is compatible with bfloa16
            image = image.cpu().permute(0, 2, 3, 1).float().numpy()
        else:
            image = self.decode_latents(latents)

        # 11. Run safety checker
        image, has_nsfw_concept = self.run_safety_checker(image, device, text_embeddings.dtype)

        # 12. Convert to PIL
        if output_type == "pil":
            image = self.numpy_to_pil(image)

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)