path: root/pipelines/stable_diffusion/vlpn_stable_diffusion.py

import inspect
import warnings
from typing import List, Optional, Union

import numpy as np
import torch
import PIL

from diffusers.configuration_utils import FrozenDict
from diffusers import AutoencoderKL, DiffusionPipeline, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNet2DConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import logging
from transformers import CLIPTextModel, CLIPTokenizer
from schedulers.scheduling_euler_a import EulerAScheduler, CFGDenoiserForward

logger = logging.get_logger(__name__)  # pylint: disable=invalid-name


def preprocess(image, w, h):
    image = image.resize((w, h), resample=PIL.Image.LANCZOS)
    image = np.array(image).astype(np.float32) / 255.0
    image = image[None].transpose(0, 3, 1, 2)
    image = torch.from_numpy(image)
    return 2.0 * image - 1.0


class VlpnStableDiffusion(DiffusionPipeline):
    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler, EulerAScheduler],
        **kwargs,
    ):
        super().__init__()

        if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1:
            warnings.warn(
                f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
                f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
                "to update the config accordingly as leaving `steps_offset` might led to incorrect results"
                " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"
                " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"
                " file",
                DeprecationWarning,
            )
            new_config = dict(scheduler.config)
            new_config["steps_offset"] = 1
            scheduler._internal_dict = FrozenDict(new_config)

        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
        )

    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
        r"""
        Enable sliced attention computation.

        When this option is enabled, the attention module will split the input tensor in slices, to compute attention
        in several steps. This is useful to save some memory in exchange for a small speed decrease.

        Args:
            slice_size (`str` or `int`, *optional*, defaults to `"auto"`):
                When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If
                a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case,
                `attention_head_dim` must be a multiple of `slice_size`.
        """
        if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            slice_size = self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(slice_size)

    def disable_attention_slicing(self):
        r"""
        Disable sliced attention computation. If `enable_attention_slicing` was previously invoked, this method will go
        back to computing attention in one step.
        """
        # set slice_size = `None` to disable `attention slicing`
        self.enable_attention_slicing(None)

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        negative_prompt: Optional[Union[str, List[str]]] = None,
        strength: float = 0.8,
        height: Optional[int] = 512,
        width: Optional[int] = 512,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        eta: Optional[float] = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[Union[torch.FloatTensor, PIL.Image.Image]] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`):
                The prompt or prompts to guide the image generation.
            strength (`float`, *optional*, defaults to 0.8):
                Conceptually, indicates how much to transform the reference `init_image`. Must be between 0 and 1.
                `init_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 `init_image`.
            height (`int`, *optional*, defaults to 512):
                The height in pixels of the generated image.
            width (`int`, *optional*, defaults to 512):
                The width in pixels of the generated image.
            num_inference_steps (`int`, *optional*, 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`, *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.
            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*):
                A [torch generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                deterministic.
            latents (`torch.FloatTensor`, *optional*):
                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`.
            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.

        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`.
        """

        if isinstance(prompt, str):
            batch_size = 1
        elif isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}")

        if negative_prompt is None:
            negative_prompt = [""] * batch_size
        elif isinstance(negative_prompt, str):
            negative_prompt = [negative_prompt] * batch_size
        elif isinstance(negative_prompt, list):
            if len(negative_prompt) != batch_size:
                raise ValueError(
                    f"`prompt` and `negative_prompt` have to be the same length, but are {len(prompt)} and {len(negative_prompt)}")
        else:
            raise ValueError(f"`negative_prompt` has to be of type `str` or `list` but is {type(negative_prompt)}")

        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.")

        if strength < 0 or strength > 1:
            raise ValueError(f"`strength` should in [0.0, 1.0] but is {strength}")

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        # get prompt text embeddings
        text_inputs = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=self.tokenizer.model_max_length,
            return_tensors="pt",
        )
        text_input_ids = text_inputs.input_ids

        if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
            removed_text = self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length:])
            logger.warning(
                "The following part of your input was truncated because CLIP can only handle sequences up to"
                f" {self.tokenizer.model_max_length} tokens: {removed_text}"
            )
            print(f"Too many tokens: {removed_text}")
            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]

        # 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
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            max_length = text_input_ids.shape[-1]
            uncond_input = self.tokenizer(
                negative_prompt, padding="max_length", max_length=max_length, return_tensors="pt"
            )
            uncond_embeddings = self.text_encoder(uncond_input.input_ids.to(self.device))[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
            text_embeddings = torch.cat([uncond_embeddings, text_embeddings])

        offset = self.scheduler.config.get("steps_offset", 0)
        init_timestep = num_inference_steps + offset
        ensure_sigma = not isinstance(latents, PIL.Image.Image)

        # get the initial random noise unless the user supplied it

        # Unlike in other pipelines, latents need to be generated in the target device
        # for 1-to-1 results reproducibility with the CompVis implementation.
        # However this currently doesn't work in `mps`.
        latents_dtype = text_embeddings.dtype
        latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
        if latents is None:
            if self.device.type == "mps":
                # randn does not exist on mps
                latents = torch.randn(latents_shape, generator=generator, device="cpu", dtype=latents_dtype).to(
                    self.device
                )
            else:
                latents = torch.randn(latents_shape, generator=generator, device=self.device, dtype=latents_dtype)
        elif isinstance(latents, PIL.Image.Image):
            latents = preprocess(latents, width, height)
            latent_dist = self.vae.encode(latents.to(self.device)).latent_dist
            latents = latent_dist.sample(generator=generator)
            latents = 0.18215 * latents

            # expand init_latents for batch_size
            latents = torch.cat([latents] * batch_size)

            # get the original timestep using init_timestep
            init_timestep = int(num_inference_steps * strength) + offset
            init_timestep = min(init_timestep, num_inference_steps)

            if isinstance(self.scheduler, LMSDiscreteScheduler):
                timesteps = torch.tensor(
                    [num_inference_steps - init_timestep] * batch_size, dtype=torch.long, device=self.device
                )
            else:
                timesteps = self.scheduler.timesteps[-init_timestep]
                timesteps = torch.tensor([timesteps] * batch_size, dtype=torch.long, device=self.device)

            # add noise to latents using the timesteps
            noise = torch.randn(latents.shape, generator=generator, device=self.device)
            latents = self.scheduler.add_noise(latents, noise, timesteps)
        else:
            if latents.shape != latents_shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
            if latents.device != self.device:
                raise ValueError(f"Unexpected latents device, got {latents.device}, expected {self.device}")

        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
        if ensure_sigma:
            if isinstance(self.scheduler, LMSDiscreteScheduler):
                latents = latents * self.scheduler.sigmas[0]
            elif isinstance(self.scheduler, EulerAScheduler):
                latents = latents * self.scheduler.sigmas[0]

        t_start = max(num_inference_steps - init_timestep + offset, 0)

        # Some schedulers like PNDM have timesteps as arrays
        # It's more optimzed to move all timesteps to correct device beforehand
        timesteps_tensor = torch.tensor(self.scheduler.timesteps[t_start:], device=self.device)

        # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
        # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
        # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
        # and should be between [0, 1]
        scheduler_step_args = set(inspect.signature(self.scheduler.step).parameters.keys())
        accepts_eta = "eta" in scheduler_step_args
        extra_step_kwargs = {}
        if accepts_eta:
            extra_step_kwargs["eta"] = eta
        accepts_generator = "generator" in scheduler_step_args
        if generator is not None and accepts_generator:
            extra_step_kwargs["generator"] = generator

        for i, t in enumerate(self.progress_bar(timesteps_tensor)):
            t_index = t_start + i

            # expand the latents if we are doing classifier free guidance
            latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents

            if isinstance(self.scheduler, LMSDiscreteScheduler):
                sigma = self.scheduler.sigmas[t_index]
                # the model input needs to be scaled to match the continuous ODE formulation in K-LMS
                latent_model_input = latent_model_input / ((sigma**2 + 1) ** 0.5)

            noise_pred = None
            if isinstance(self.scheduler, EulerAScheduler):
                sigma = self.scheduler.sigmas[t].reshape(1)
                sigma_in = torch.cat([sigma] * latent_model_input.shape[0])
                noise_pred = CFGDenoiserForward(self.unet, latent_model_input, sigma_in,
                                                text_embeddings, guidance_scale, quantize=True, DSsigmas=self.scheduler.DSsigmas)
            else:
                # 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
            if isinstance(self.scheduler, LMSDiscreteScheduler):
                latents = self.scheduler.step(noise_pred, t_index, latents, **extra_step_kwargs).prev_sample
            elif isinstance(self.scheduler, EulerAScheduler):
                latents = self.scheduler.step(noise_pred, t_index, t_index + 1,
                                              latents, **extra_step_kwargs).prev_sample
            else:
                latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample

        # scale and decode the image latents with vae
        latents = 1 / 0.18215 * latents
        image = self.vae.decode(latents).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)

        if not return_dict:
            return (image, None)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=None)