pipeline_stable_diffusion_xl_img2img.py

#  Copyright 2023 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,
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#  See the License for the specific language governing permissions and
#  limitations under the License.

import inspect
import logging
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
from diffusers.pipelines.stable_diffusion_xl import StableDiffusionXLPipelineOutput

from .pipeline_utils import DiffusionPipelineMixin, rescale_noise_cfg


logger = logging.getLogger(__name__)


class StableDiffusionXLImg2ImgPipelineMixin(DiffusionPipelineMixin):
    # Adapted from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.encode_prompt
    def _encode_prompt(
        self,
        prompt: Union[str, List[str]],
        num_images_per_prompt: int,
        do_classifier_free_guidance: bool,
        negative_prompt: Optional[Union[str, list]],
        prompt_embeds: Optional[np.ndarray] = None,
        negative_prompt_embeds: Optional[np.ndarray] = None,
        pooled_prompt_embeds: Optional[np.ndarray] = None,
        negative_pooled_prompt_embeds: Optional[np.ndarray] = None,
    ):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`Union[str, List[str]]`):
                prompt to be encoded
            num_images_per_prompt (`int`):
                number of images that should be generated per prompt
            do_classifier_free_guidance (`bool`):
                whether to use classifier free guidance or not
            negative_prompt (`Optional[Union[str, list]]`):
                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`).
            prompt_embeds (`Optional[np.ndarray]`, defaults to `None`):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`Optional[np.ndarray]`, defaults to `None`):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            pooled_prompt_embeds (`Optional[np.ndarray]`, defaults to `None`):
                Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting.
                If not provided, pooled text embeddings will be generated from `prompt` input argument.
            negative_pooled_prompt_embeds (`Optional[np.ndarray]`, defaults to `None`):
                Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt`
                input argument.
        """
        if isinstance(prompt, str):
            batch_size = 1
        elif isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        # Define tokenizers and text encoders
        tokenizers = [self.tokenizer, self.tokenizer_2] if self.tokenizer is not None else [self.tokenizer_2]
        text_encoders = (
            [self.text_encoder, self.text_encoder_2] if self.text_encoder is not None else [self.text_encoder_2]
        )

        if prompt_embeds is None:
            prompt_embeds_list = []
            for tokenizer, text_encoder in zip(tokenizers, text_encoders):
                # get prompt text embeddings
                text_inputs = tokenizer(
                    prompt,
                    padding="max_length",
                    max_length=tokenizer.model_max_length,
                    truncation=True,
                    return_tensors="np",
                )
                text_input_ids = text_inputs.input_ids
                untruncated_ids = tokenizer(prompt, padding="longest", return_tensors="np").input_ids

                if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not np.array_equal(
                    text_input_ids, untruncated_ids
                ):
                    removed_text = tokenizer.batch_decode(untruncated_ids[:, tokenizer.model_max_length - 1 : -1])
                    logger.warning(
                        "The following part of your input was truncated because CLIP can only handle sequences up to"
                        f" {tokenizer.model_max_length} tokens: {removed_text}"
                    )

                prompt_embeds = text_encoder(
                    input_ids=text_input_ids.astype(text_encoder.input_dtype.get("input_ids", np.int32))
                )
                pooled_prompt_embeds = prompt_embeds[0]
                prompt_embeds = prompt_embeds[-2]
                prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)
                prompt_embeds_list.append(prompt_embeds)

            prompt_embeds = np.concatenate(prompt_embeds_list, axis=-1)

        # get unconditional embeddings for classifier free guidance
        zero_out_negative_prompt = negative_prompt is None and self.config["force_zeros_for_empty_prompt"]
        if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt:
            negative_prompt_embeds = np.zeros_like(prompt_embeds)
            negative_pooled_prompt_embeds = np.zeros_like(pooled_prompt_embeds)
        elif do_classifier_free_guidance and negative_prompt_embeds is None:
            negative_prompt = negative_prompt or ""
            if prompt is not None and type(prompt) is not type(negative_prompt):
                raise TypeError(
                    f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !="
                    f" {type(prompt)}."
                )
            elif isinstance(negative_prompt, str):
                uncond_tokens = [negative_prompt]
            elif batch_size != len(negative_prompt):
                raise ValueError(
                    f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:"
                    f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
                    " the batch size of `prompt`."
                )
            else:
                uncond_tokens = negative_prompt

            negative_prompt_embeds_list = []
            for tokenizer, text_encoder in zip(tokenizers, text_encoders):
                max_length = prompt_embeds.shape[1]
                uncond_input = tokenizer(
                    uncond_tokens,
                    padding="max_length",
                    max_length=max_length,
                    truncation=True,
                    return_tensors="np",
                )

                negative_prompt_embeds = text_encoder(
                    input_ids=uncond_input.input_ids.astype(text_encoder.input_dtype.get("input_ids", np.int32))
                )
                negative_pooled_prompt_embeds = negative_prompt_embeds[0]
                negative_prompt_embeds = negative_prompt_embeds[-2]
                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                negative_prompt_embeds = np.repeat(negative_prompt_embeds, num_images_per_prompt, axis=0)
                # For classifier free guidance, we need to do two forward passes.
                # Here we concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                negative_prompt_embeds_list.append(negative_prompt_embeds)
            negative_prompt_embeds = np.concatenate(negative_prompt_embeds_list, axis=-1)

        pooled_prompt_embeds = np.repeat(pooled_prompt_embeds, num_images_per_prompt, axis=0)
        negative_pooled_prompt_embeds = np.repeat(negative_pooled_prompt_embeds, num_images_per_prompt, axis=0)

        return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl_img2img.StableDiffusionXLImg2ImgPipeline.check_inputs
    def check_inputs(
        self,
        prompt: Union[str, List[str]],
        strength: float,
        callback_steps: int,
        negative_prompt: Optional[str] = None,
        prompt_embeds: Optional[np.ndarray] = None,
        negative_prompt_embeds: Optional[np.ndarray] = None,
    ):
        if strength < 0 or strength > 1:
            raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}")

        if (callback_steps is None) or (
            callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0)
        ):
            raise ValueError(
                f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
                f" {type(callback_steps)}."
            )

        if prompt is not None and prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to"
                " only forward one of the two."
            )
        elif prompt is None and prompt_embeds is None:
            raise ValueError(
                "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined."
            )
        elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)):
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if negative_prompt is not None and negative_prompt_embeds is not None:
            raise ValueError(
                f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:"
                f" {negative_prompt_embeds}. Please make sure to only forward one of the two."
            )

        if prompt_embeds is not None and negative_prompt_embeds is not None:
            if prompt_embeds.shape != negative_prompt_embeds.shape:
                raise ValueError(
                    "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but"
                    f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`"
                    f" {negative_prompt_embeds.shape}."
                )

    def get_timesteps(self, num_inference_steps, strength):
        # get the original timestep using init_timestep
        init_timestep = min(int(num_inference_steps * strength), num_inference_steps)
        t_start = max(num_inference_steps - init_timestep, 0)
        timesteps = self.scheduler.timesteps[t_start * self.scheduler.order :].numpy()

        return timesteps, num_inference_steps - t_start

    # Adapted from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents
    def prepare_latents(self, image, timestep, batch_size, num_images_per_prompt, dtype, generator=None):
        batch_size = batch_size * num_images_per_prompt

        if image.shape[1] == 4:
            init_latents = image
        else:
            init_latents = self.vae_encoder(sample=image)[0] * self.vae_decoder.config.get("scaling_factor", 0.18215)

        if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
            # expand init_latents for batch_size
            additional_image_per_prompt = batch_size // init_latents.shape[0]
            init_latents = np.concatenate([init_latents] * additional_image_per_prompt, axis=0)
        elif batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] != 0:
            raise ValueError(
                f"Cannot duplicate `image` of batch size {init_latents.shape[0]} to {batch_size} text prompts."
            )
        else:
            init_latents = np.concatenate([init_latents], axis=0)

        # add noise to latents using the timesteps
        noise = generator.randn(*init_latents.shape).astype(dtype)
        init_latents = self.scheduler.add_noise(
            torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timestep)
        )
        return init_latents.numpy()

    def _get_add_time_ids(
        self, original_size, crops_coords_top_left, target_size, aesthetic_score, negative_aesthetic_score, dtype
    ):
        if self.config.get("requires_aesthetics_score"):
            add_time_ids = (original_size + crops_coords_top_left + (aesthetic_score,),)
            add_neg_time_ids = (original_size + crops_coords_top_left + (negative_aesthetic_score,),)
        else:
            add_time_ids = (original_size + crops_coords_top_left + target_size,)
            add_neg_time_ids = (original_size + crops_coords_top_left + target_size,)

        add_time_ids = np.array(add_time_ids, dtype=dtype)
        add_neg_time_ids = np.array(add_neg_time_ids, dtype=dtype)

        return add_time_ids, add_neg_time_ids

    # Adapted from diffusers.pipelines.stable_diffusion_xl.pipeline_stable_diffusion_xl.StableDiffusionXLPipeline.__call__
    def __call__(
        self,
        prompt: Optional[Union[str, List[str]]] = None,
        image: Union[np.ndarray, PIL.Image.Image] = None,
        strength: float = 0.3,
        num_inference_steps: int = 50,
        guidance_scale: float = 5.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: int = 1,
        eta: float = 0.0,
        generator: Optional[np.random.RandomState] = None,
        latents: Optional[np.ndarray] = None,
        prompt_embeds: Optional[np.ndarray] = None,
        negative_prompt_embeds: Optional[np.ndarray] = None,
        pooled_prompt_embeds: Optional[np.ndarray] = None,
        negative_pooled_prompt_embeds: Optional[np.ndarray] = None,
        output_type: str = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
        callback_steps: int = 1,
        cross_attention_kwargs: Optional[Dict[str, Any]] = None,
        guidance_rescale: float = 0.0,
        original_size: Optional[Tuple[int, int]] = None,
        crops_coords_top_left: Tuple[int, int] = (0, 0),
        target_size: Optional[Tuple[int, int]] = None,
        aesthetic_score: float = 6.0,
        negative_aesthetic_score: float = 2.5,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`Optional[Union[str, List[str]]]`, defaults to None):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            image (`Union[np.ndarray, PIL.Image.Image]`):
                `Image`, or tensor representing an image batch which will be upscaled.
            strength (`float`, defaults to 0.8):
                Conceptually, indicates how much to transform the reference `image`. Must be between 0 and 1. `image`
                will be used as a starting point, adding more noise to it the larger the `strength`. The number of
                denoising steps depends on the amount of noise initially added. When `strength` is 1, added noise will
                be maximum and the denoising process will run for the full number of iterations specified in
                `num_inference_steps`. A value of 1, therefore, essentially ignores `image`.
            num_inference_steps (`int`, defaults to 50):
                The number of denoising steps. More denoising steps usually lead to a higher quality image at the
                expense of slower inference.
            guidance_scale (`float`, defaults to 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 (`Optional[Union[str, list]]`):
                The prompt or prompts not to guide the image generation. If not defined, one has to pass
                `negative_prompt_embeds`. instead. Ignored when not using guidance (i.e., ignored if `guidance_scale`
                is less than `1`).
            num_images_per_prompt (`int`, defaults to 1):
                The number of images to generate per prompt.
            eta (`float`, 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 (`Optional[np.random.RandomState]`, defaults to `None`)::
                A np.random.RandomState to make generation deterministic.
            latents (`Optional[np.ndarray]`, defaults to `None`):
                Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image
                generation. Can be used to tweak the same generation with different prompts. If not provided, a latents
                tensor will ge generated by sampling using the supplied random `generator`.
            prompt_embeds (`Optional[np.ndarray]`, defaults to `None`):
                Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not
                provided, text embeddings will be generated from `prompt` input argument.
            negative_prompt_embeds (`Optional[np.ndarray]`, defaults to `None`):
                Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt
                weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input
                argument.
            output_type (`str`, 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`, defaults to `True`):
                Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] instead of a
                plain tuple.
            callback (Optional[Callable], defaults to `None`):
                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`, defaults to 1):
                The frequency at which the `callback` function will be called. If not specified, the callback will be
                called at every step.
            guidance_rescale (`float`, defaults to 0.7):
                Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are
                Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of
                [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf).
                Guidance rescale factor should fix overexposure when using zero terminal SNR.

        Returns:
            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] or `tuple`:
            [`~pipelines.stable_diffusion.StableDiffusionXLPipelineOutput`] 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`.
        """
        # 0. Check inputs. Raise error if not correct
        self.check_inputs(prompt, strength, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)

        # 1. Define call parameters
        if isinstance(prompt, str):
            batch_size = 1
        elif isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if generator is None:
            generator = np.random

        # 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

        # 2. Encode input prompt
        (
            prompt_embeds,
            negative_prompt_embeds,
            pooled_prompt_embeds,
            negative_pooled_prompt_embeds,
        ) = self._encode_prompt(
            prompt,
            num_images_per_prompt,
            do_classifier_free_guidance,
            negative_prompt,
            prompt_embeds=prompt_embeds,
            negative_prompt_embeds=negative_prompt_embeds,
            pooled_prompt_embeds=pooled_prompt_embeds,
            negative_pooled_prompt_embeds=negative_pooled_prompt_embeds,
        )

        # 3. Preprocess image
        image = self.image_processor.preprocess(image)

        # 4. Prepare timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        timesteps, num_inference_steps = self.get_timesteps(num_inference_steps, strength)
        latent_timestep = np.repeat(timesteps[:1], batch_size * num_images_per_prompt, axis=0)
        timestep_dtype = self.unet.input_dtype.get("timestep", np.float32)

        latents_dtype = prompt_embeds.dtype
        image = image.astype(latents_dtype)

        # 5. Prepare latent variables
        latents = self.prepare_latents(
            image, latent_timestep, batch_size, num_images_per_prompt, latents_dtype, generator
        )

        # 6. Prepare extra step kwargs
        extra_step_kwargs = {}
        accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_eta:
            extra_step_kwargs["eta"] = eta

        height, width = latents.shape[-2:]
        height = height * self.vae_scale_factor
        width = width * self.vae_scale_factor
        original_size = original_size or (height, width)
        target_size = target_size or (height, width)

        # 8. Prepare added time ids & embeddings
        add_text_embeds = pooled_prompt_embeds
        add_time_ids, add_neg_time_ids = self._get_add_time_ids(
            original_size,
            crops_coords_top_left,
            target_size,
            aesthetic_score,
            negative_aesthetic_score,
            dtype=prompt_embeds.dtype,
        )

        if do_classifier_free_guidance:
            prompt_embeds = np.concatenate((negative_prompt_embeds, prompt_embeds), axis=0)
            add_text_embeds = np.concatenate((negative_pooled_prompt_embeds, add_text_embeds), axis=0)
            add_time_ids = np.concatenate((add_time_ids, add_time_ids), axis=0)
        add_time_ids = np.repeat(add_time_ids, batch_size * num_images_per_prompt, axis=0)

        # 8. Denoising loop
        num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order
        for i, t in enumerate(self.progress_bar(timesteps)):
            # expand the latents if we are doing classifier free guidance
            latent_model_input = np.concatenate([latents] * 2) if do_classifier_free_guidance else latents
            latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
            latent_model_input = latent_model_input.cpu().numpy()

            # predict the noise residual
            timestep = np.array([t], dtype=timestep_dtype)
            noise_pred = self.unet(
                sample=latent_model_input,
                timestep=timestep,
                encoder_hidden_states=prompt_embeds,
                text_embeds=add_text_embeds,
                time_ids=add_time_ids,
            )
            noise_pred = noise_pred[0]

            # perform guidance
            if do_classifier_free_guidance:
                noise_pred_uncond, noise_pred_text = np.split(noise_pred, 2)
                noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                if guidance_rescale > 0.0:
                    # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
                    noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)

            # compute the previous noisy sample x_t -> x_t-1
            scheduler_output = self.scheduler.step(
                torch.from_numpy(noise_pred), t, torch.from_numpy(latents), **extra_step_kwargs
            )
            latents = scheduler_output.prev_sample.numpy()

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

        if output_type == "latent":
            image = latents
        else:
            latents /= self.vae_decoder.config.get("scaling_factor", 0.18215)
            # it seems likes there is a strange result for using half-precision vae decoder if batchsize>1
            image = np.concatenate(
                [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
            )
            # apply watermark if available
            if self.watermark is not None:
                image = self.watermark.apply_watermark(image)
            image = self.image_processor.postprocess(image, output_type=output_type)

        if not return_dict:
            return (image,)

        return StableDiffusionXLPipelineOutput(images=image)