path: root/pipelines/stable_diffusion/clip_guided_stable_diffusion.py

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

import torch
from torch import nn
from torch.nn import functional as F

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 torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from schedulers.scheduling_euler_a import EulerAScheduler, CFGDenoiserForward

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


class MakeCutouts(nn.Module):
    def __init__(self, cut_size, cut_power=1.0):
        super().__init__()

        self.cut_size = cut_size
        self.cut_power = cut_power

    def forward(self, pixel_values, num_cutouts):
        sideY, sideX = pixel_values.shape[2:4]
        max_size = min(sideX, sideY)
        min_size = min(sideX, sideY, self.cut_size)
        cutouts = []
        for _ in range(num_cutouts):
            size = int(torch.rand([]) ** self.cut_power * (max_size - min_size) + min_size)
            offsetx = torch.randint(0, sideX - size + 1, ())
            offsety = torch.randint(0, sideY - size + 1, ())
            cutout = pixel_values[:, :, offsety: offsety + size, offsetx: offsetx + size]
            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
        return torch.cat(cutouts)


def spherical_dist_loss(x, y):
    x = F.normalize(x, dim=-1)
    y = F.normalize(y, dim=-1)
    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)


def set_requires_grad(model, value):
    for param in model.parameters():
        param.requires_grad = value


class CLIPGuidedStableDiffusion(DiffusionPipeline):
    """CLIP guided stable diffusion based on the amazing repo by @crowsonkb and @Jack000
    - https://github.com/Jack000/glid-3-xl
    - https://github.dev/crowsonkb/k-diffusion
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        clip_model: CLIPModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        feature_extractor: CLIPFeatureExtractor,
        **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,
            clip_model=clip_model,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            feature_extractor=feature_extractor,
        )

        self.normalize = transforms.Normalize(mean=feature_extractor.image_mean, std=feature_extractor.image_std)
        self.make_cutouts = MakeCutouts(feature_extractor.size)

        set_requires_grad(self.text_encoder, False)
        set_requires_grad(self.clip_model, False)

    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)

    def freeze_vae(self):
        set_requires_grad(self.vae, False)

    def unfreeze_vae(self):
        set_requires_grad(self.vae, True)

    def freeze_unet(self):
        set_requires_grad(self.unet, False)

    def unfreeze_unet(self):
        set_requires_grad(self.unet, True)

    @torch.enable_grad()
    def cond_fn(
        self,
        latents,
        timestep,
        index,
        text_embeddings,
        noise_pred_original,
        text_embeddings_clip,
        clip_guidance_scale,
        num_cutouts,
        use_cutouts=True,
    ):
        latents = latents.detach().requires_grad_()

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

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

        if isinstance(self.scheduler, PNDMScheduler):
            alpha_prod_t = self.scheduler.alphas_cumprod[timestep]
            beta_prod_t = 1 - alpha_prod_t
            # compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            pred_original_sample = (latents - beta_prod_t ** (0.5) * noise_pred) / alpha_prod_t ** (0.5)

            fac = torch.sqrt(beta_prod_t)
            sample = pred_original_sample * (fac) + latents * (1 - fac)
        elif isinstance(self.scheduler, LMSDiscreteScheduler):
            sigma = self.scheduler.sigmas[index]
            sample = latents - sigma * noise_pred
        else:
            raise ValueError(f"scheduler type {type(self.scheduler)} not supported")

        sample = 1 / 0.18215 * sample
        image = self.vae.decode(sample).sample
        image = (image / 2 + 0.5).clamp(0, 1)

        if use_cutouts:
            image = self.make_cutouts(image, num_cutouts)
        else:
            image = transforms.Resize(self.feature_extractor.size)(image)
        image = self.normalize(image)

        image_embeddings_clip = self.clip_model.get_image_features(image).float()
        image_embeddings_clip = image_embeddings_clip / image_embeddings_clip.norm(p=2, dim=-1, keepdim=True)

        if use_cutouts:
            dists = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip)
            dists = dists.view([num_cutouts, sample.shape[0], -1])
            loss = dists.sum(2).mean(0).sum() * clip_guidance_scale
        else:
            loss = spherical_dist_loss(image_embeddings_clip, text_embeddings_clip).mean() * clip_guidance_scale

        grads = -torch.autograd.grad(loss, latents)[0]

        if isinstance(self.scheduler, LMSDiscreteScheduler):
            latents = latents.detach() + grads * (sigma**2)
            noise_pred = noise_pred_original
        else:
            noise_pred = noise_pred_original - torch.sqrt(beta_prod_t) * grads
        return noise_pred, latents

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        negative_prompt: Optional[Union[str, List[str]]] = None,
        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,
        clip_guidance_scale: Optional[float] = 100,
        clip_prompt: Optional[Union[str, List[str]]] = None,
        num_cutouts: Optional[int] = 4,
        use_cutouts: Optional[bool] = True,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = 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.
            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}.")

        # 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}"
            )
            text_input_ids = text_input_ids[:, : self.tokenizer.model_max_length]
        text_embeddings = self.text_encoder(text_input_ids.to(self.device))[0]

        if clip_guidance_scale > 0:
            if clip_prompt is not None:
                clip_text_inputs = self.tokenizer(
                    clip_prompt,
                    padding="max_length",
                    max_length=self.tokenizer.model_max_length,
                    truncation=True,
                    return_tensors="pt",
                )
                clip_text_input_ids = clip_text_inputs.input_ids
            else:
                clip_text_input_ids = text_input_ids
            text_embeddings_clip = self.clip_model.get_text_features(clip_text_input_ids.to(self.device))
            text_embeddings_clip = text_embeddings_clip / text_embeddings_clip.norm(p=2, dim=-1, keepdim=True)

        # 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])

        # 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_device = "cpu" if self.device.type == "mps" else self.device
        latents_shape = (batch_size, self.unet.in_channels, height // 8, width // 8)
        if latents is None:
            latents = torch.randn(
                latents_shape,
                generator=generator,
                device=latents_device,
                dtype=text_embeddings.dtype,
            )
        else:
            if latents.shape != latents_shape:
                raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
        latents = latents.to(self.device)

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

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

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

        # 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)):
            # 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[i]
                # 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 = t.reshape(1)
                sigma_in = torch.cat([sigma] * 2)
                # noise_pred = model(latent_model_input,sigma_in,uncond_embeddings, text_embeddings,guidance_scale)
                noise_pred = CFGDenoiserForward(self.unet, latent_model_input, sigma_in,
                                                text_embeddings, guidance_scale, DSsigmas=self.scheduler.DSsigmas)
                # noise_pred = self.unet(latent_model_input, sigma_in, encoder_hidden_states=text_embeddings).sample
            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)

            # perform clip guidance
            if clip_guidance_scale > 0:
                text_embeddings_for_guidance = (
                    text_embeddings.chunk(2)[1] if do_classifier_free_guidance else text_embeddings
                )
                noise_pred, latents = self.cond_fn(
                    latents,
                    t,
                    i,
                    text_embeddings_for_guidance,
                    noise_pred,
                    text_embeddings_clip,
                    clip_guidance_scale,
                    num_cutouts,
                    use_cutouts,
                )

            # compute the previous noisy sample x_t -> x_t-1
            if isinstance(self.scheduler, LMSDiscreteScheduler):
                latents = self.scheduler.step(noise_pred, i, latents, **extra_step_kwargs).prev_sample
            elif isinstance(self.scheduler, EulerAScheduler):
                if i < self.scheduler.timesteps.shape[0] - 1:  # avoid out of bound error
                    t_prev = self.scheduler.timesteps[i+1]
                    latents = self.scheduler.step(noise_pred, t, t_prev, 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)