pipeline_stable_diffusion_img2img_ait.py

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# flakes8: noqa
import inspect
import os
from typing import List, Optional, Union

import numpy as np

import PIL
import torch
from aitemplate.compiler import Model

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionImg2ImgPipeline,
    UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion import (
    StableDiffusionPipelineOutput,
    StableDiffusionSafetyChecker,
)
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer


def preprocess(image):
    w, h = image.size
    w, h = map(lambda x: x - x % 32, (w, h))  # resize to integer multiple of 32
    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 StableDiffusionImg2ImgAITPipeline(StableDiffusionImg2ImgPipeline):
    r"""
    Pipeline for text-guided image to image generation using Stable Diffusion.

    This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
    library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)

    Args:
        vae ([`AutoencoderKL`]):
            Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations.
        text_encoder ([`CLIPTextModel`]):
            Frozen text-encoder. Stable Diffusion uses the text portion of
            [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically
            the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant.
        tokenizer (`CLIPTokenizer`):
            Tokenizer of class
            [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
        unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents.
        scheduler ([`SchedulerMixin`]):
            A scheduler to be used in combination with `unet` to denoise the encoded image latens. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offsensive or harmful.
            Please, refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for details.
        feature_extractor ([`CLIPFeatureExtractor`]):
            Model that extracts features from generated images to be used as inputs for the `safety_checker`.
    """

    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor,
    ):
        # super().__init__()
        super().__init__(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        scheduler = scheduler.set_format("pt")
        self.register_modules(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )

        workdir = "tmp/"
        self.clip_ait_exe = self.init_ait_module(
            model_name="CLIPTextModel", workdir=workdir
        )
        self.unet_ait_exe = self.init_ait_module(
            model_name="UNet2DConditionModel", workdir=workdir
        )
        self.vae_ait_exe = self.init_ait_module(
            model_name="AutoencoderKL", workdir=workdir
        )

    def init_ait_module(
        self,
        model_name,
        workdir,
    ):
        mod = Model(os.path.join(workdir, model_name, "test.so"))
        return mod

    def unet_inference(self, latent_model_input, timesteps, encoder_hidden_states):
        exe_module = self.unet_ait_exe
        timesteps_pt = timesteps.expand(latent_model_input.shape[0])
        inputs = {
            "input0": latent_model_input.permute((0, 2, 3, 1))
            .contiguous()
            .cuda()
            .half(),
            "input1": timesteps_pt.cuda().half(),
            "input2": encoder_hidden_states.cuda().half(),
        }
        ys = []
        num_ouputs = len(exe_module.get_output_name_to_index_map())
        for i in range(num_ouputs):
            shape = exe_module.get_output_maximum_shape(i)
            ys.append(torch.empty(shape).cuda().half())
        exe_module.run_with_tensors(inputs, ys, graph_mode=False)
        noise_pred = ys[0].permute((0, 3, 1, 2)).float()
        return noise_pred

    def clip_inference(self, input_ids, seqlen=64):
        exe_module = self.clip_ait_exe
        bs = input_ids.shape[0]
        position_ids = torch.arange(seqlen).expand((bs, -1)).cuda()
        inputs = {
            "input0": input_ids,
            "input1": position_ids,
        }
        ys = []
        num_ouputs = len(exe_module.get_output_name_to_index_map())
        for i in range(num_ouputs):
            shape = exe_module.get_output_maximum_shape(i)
            ys.append(torch.empty(shape).cuda().half())
        exe_module.run_with_tensors(inputs, ys, graph_mode=False)
        return ys[0].float()

    def vae_inference(self, vae_input):
        exe_module = self.vae_ait_exe
        inputs = [torch.permute(vae_input, (0, 2, 3, 1)).contiguous().cuda().half()]
        ys = []
        num_ouputs = len(exe_module.get_output_name_to_index_map())
        for i in range(num_ouputs):
            shape = exe_module.get_output_maximum_shape(i)
            ys.append(torch.empty(shape).cuda().half())
        exe_module.run_with_tensors(inputs, ys, graph_mode=False)
        vae_out = ys[0].permute((0, 3, 1, 2)).float()
        return vae_out

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]],
        init_image: Union[torch.FloatTensor, PIL.Image.Image],
        strength: float = 0.8,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        eta: Optional[float] = 0.0,
        generator: Optional[torch.Generator] = 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.
            init_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.
            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`.
            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. This parameter will be modulated by `strength`.
            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.
            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 strength < 0 or strength > 1:
            raise ValueError(
                f"The value of strength should in [0.0, 1.0] but is {strength}"
            )

        # set timesteps
        accepts_offset = "offset" in set(
            inspect.signature(self.scheduler.set_timesteps).parameters.keys()
        )
        extra_set_kwargs = {}
        offset = 0
        if accepts_offset:
            offset = 1
            extra_set_kwargs["offset"] = 1

        self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)

        if isinstance(init_image, PIL.Image.Image):
            init_image = preprocess(init_image)

        # encode the init image into latents and scale the latents
        init_latent_dist = self.vae.encode(init_image.to(self.device)).latent_dist
        init_latents = init_latent_dist.sample(generator=generator)
        init_latents = 0.18215 * init_latents

        # expand init_latents for batch_size
        init_latents = torch.cat([init_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(init_latents.shape, generator=generator, device=self.device)
        init_latents = self.scheduler.add_noise(init_latents, noise, timesteps).to(
            self.device
        )

        # get prompt text embeddings
        text_input = self.tokenizer(
            prompt,
            padding="max_length",
            max_length=64,  # self.tokenizer.model_max_length,
            truncation=True,
            return_tensors="pt",
        )
        text_embeddings = self.clip_inference(text_input.input_ids.to(self.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
        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            max_length = text_input.input_ids.shape[-1]
            uncond_input = self.tokenizer(
                [""] * batch_size,
                padding="max_length",
                max_length=max_length,
                return_tensors="pt",
            )
            uncond_embeddings = self.clip_inference(
                uncond_input.input_ids.to(self.device)
            )

            # 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])

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

        latents = init_latents

        t_start = max(num_inference_steps - init_timestep + offset, 0)
        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps[t_start:])):
            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 we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
            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)
                latent_model_input = latent_model_input.to(self.unet.dtype)
                t = t.to(self.unet.dtype)

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

            # 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
            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_inference(latents)

        image = (image / 2 + 0.5).clamp(0, 1)
        image = image.cpu().permute(0, 2, 3, 1).numpy()

        # run safety checker
        safety_cheker_input = self.feature_extractor(
            self.numpy_to_pil(image), return_tensors="pt"
        ).to(self.device)
        image, has_nsfw_concept = self.safety_checker(
            images=image, clip_input=safety_cheker_input.pixel_values
        )

        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
        )