patchedstabledifftoonnx_withou_modelort/pipeline_onnx_stable_diffusion_instruct_pix2pix.py

# Copyright 2023 The InstructPix2Pix Authors and The HuggingFace Team.
# Converted for use with ONNX as part of https://github.com/Amblyopius/Stable-Diffusion-ONNX-FP16

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

import numpy as np
import PIL
import torch
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer

try:
    from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE
except ImportError:
    ORT_TO_NP_TYPE = {
        "tensor(bool)": np.bool_,
        "tensor(int8)": np.int8,
        "tensor(uint8)": np.uint8,
        "tensor(int16)": np.int16,
        "tensor(uint16)": np.uint16,
        "tensor(int32)": np.int32,
        "tensor(uint32)": np.uint32,
        "tensor(int64)": np.int64,
        "tensor(uint64)": np.uint64,
        "tensor(float16)": np.float16,
        "tensor(float)": np.float32,
        "tensor(double)": np.float64,
    }

from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, SchedulerMixin
from diffusers.models import AutoencoderKL, UNet2DConditionModel
from diffusers.schedulers import KarrasDiffusionSchedulers, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import (
    PIL_INTERPOLATION,
    deprecate,
    logging,
    randn_tensor,
)
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput


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


# Simplified and ONNX specific version (only allows 1 image, np over torch)
def preprocess(image):
    if isinstance(image, np.ndarray):
        return image
        
    w, h = image.size
    w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
    image = np.array(image.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :]
    image = np.array(image).astype(np.float32) / 255.0
    image = image.transpose(0, 3, 1, 2)
    image = 2.0 * image - 1.0
    return image


class OnnxStableDiffusionInstructPix2PixPipeline(DiffusionPipeline):
    r"""
    Pipeline for pixel-level image editing by following text instructions. Based on 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 latents. Can be one of
            [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`].
        safety_checker ([`StableDiffusionSafetyChecker`]):
            Classification module that estimates whether generated images could be considered offensive 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`.
    """
    vae_encoder: OnnxRuntimeModel
    vae_decoder: OnnxRuntimeModel
    text_encoder: OnnxRuntimeModel
    tokenizer: CLIPTokenizer
    unet: OnnxRuntimeModel
    scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]
    safety_checker: OnnxRuntimeModel
    feature_extractor: CLIPFeatureExtractor
    _optional_components = ["safety_checker", "feature_extractor"]

    def __init__(
        self,
        vae_encoder: OnnxRuntimeModel,
        vae_decoder: OnnxRuntimeModel,
        text_encoder: OnnxRuntimeModel,
        tokenizer: CLIPTokenizer,
        unet: OnnxRuntimeModel,
        scheduler: KarrasDiffusionSchedulers,
        safety_checker: OnnxRuntimeModel,
        feature_extractor: CLIPFeatureExtractor,
        requires_safety_checker: bool = True,
    ):
        super().__init__()
        self.unet_in_channels = 8
        self.vae_scale_factor = 8

        if safety_checker is None and requires_safety_checker:
            logger.warning(
                f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
                " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"
                " results in services or applications open to the public. Both the diffusers team and Hugging Face"
                " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
                " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
                " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
            )

        if safety_checker is not None and feature_extractor is None:
            raise ValueError(
                "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
                " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
            )

        self.register_modules(
            vae_encoder=vae_encoder,
            vae_decoder=vae_decoder,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
        )
        #self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1)
        self.register_to_config(requires_safety_checker=requires_safety_checker)

    @torch.no_grad()
    def __call__(
        self,
        prompt: Union[str, List[str]] = None,
        image: Union[np.ndarray, PIL.Image.Image] = None,
        num_inference_steps: int = 100,
        guidance_scale: float = 7.5,
        image_guidance_scale: float = 1.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[np.random.RandomState] = None,
        latents: Optional[np.ndarray] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, np.ndarray], None]] = None,
        callback_steps: int = 1,
    ):
        r"""
        Function invoked when calling the pipeline for generation.

        Args:
            prompt (`str` or `List[str]`, *optional*):
                The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`.
                instead.
            image (`PIL.Image.Image`):
                `Image`, or tensor representing an image batch which will be repainted according to `prompt`.
            num_inference_steps (`int`, *optional*, defaults to 100):
                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. This pipeline requires a value of at least `1`.
            image_guidance_scale (`float`, *optional*, defaults to 1.5):
                Image guidance scale is to push the generated image towards the inital image `image`. Image guidance
                scale is enabled by setting `image_guidance_scale > 1`. Higher image guidance scale encourages to
                generate images that are closely linked to the source image `image`, usually at the expense of lower
                image quality. This pipeline requires a value of at least `1`.
            negative_prompt (`str` or `List[str]`, *optional*):
                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`, *optional*, defaults to 1):
                The number of images to generate per prompt.
            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.
            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`.
            prompt_embeds (`torch.FloatTensor`, *optional*):
                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 (`torch.FloatTensor`, *optional*):
                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`, *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.

        Examples:

        ```py
        >>> import PIL
        >>> import requests
        >>> import torch
        >>> from io import BytesIO

        >>> from diffusers import StableDiffusionInstructPix2PixPipeline


        >>> def download_image(url):
        ...     response = requests.get(url)
        ...     return PIL.Image.open(BytesIO(response.content)).convert("RGB")


        >>> img_url = "https://huggingface.co/datasets/diffusers/diffusers-images-docs/resolve/main/mountain.png"

        >>> image = download_image(img_url).resize((512, 512))

        >>> pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(
        ...     "timbrooks/instruct-pix2pix", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to("cuda")

        >>> prompt = "make the mountains snowy"
        >>> image = pipe(prompt=prompt, image=image).images[0]
        ```

        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`.
        """
        
        # We need a deterministic torch generator for schedulers if a (likely seeded) generator was provided
        
        if generator:
            torch_seed = generator.randint(2147483647)
            torch_gen = torch.Generator().manual_seed(torch_seed)
        else:
            generator = np.random
            torch_gen = None
        
        # 0. Check inputs
        self.check_inputs(prompt, callback_steps)

        if image is None:
            raise ValueError("`image` input cannot be undefined.")

        # 1. Define call parameters
        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)}")

        # 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 and image_guidance_scale >= 1.0
        # check if scheduler is in sigmas space
        scheduler_is_in_sigma_space = hasattr(self.scheduler, "sigmas")

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

        # 3. Preprocess image
        image = preprocess(image)
        height, width = image.shape[-2:]

        # 4. set timesteps
        self.scheduler.set_timesteps(num_inference_steps)
        timesteps = self.scheduler.timesteps

        # 5. Prepare Image latents
        latents_dtype = prompt_embeds.dtype
        image = image.astype(latents_dtype)
        # encode the init image into latents and scale the latents
        image_latents = self.vae_encoder(sample=image)[0]
        if do_classifier_free_guidance:
            uncond_image_latents = np.zeros_like(image_latents)
            image_latents = np.concatenate((image_latents, image_latents, uncond_image_latents), axis=0)

        # 6. Prepare latent variables
        latents_dtype = prompt_embeds.dtype
        latents_shape = (batch_size * num_images_per_prompt, 4, height // 8, width // 8)
        if latents is None:
            latents = generator.randn(*latents_shape).astype(latents_dtype)
        elif latents.shape != latents_shape:
            raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}")
        latents = latents * self.scheduler.init_noise_sigma.numpy()

        # 7. Check that shapes of latents and image match the UNet channels
        num_channels_image = image_latents.shape[1]
        if 4+ num_channels_image != self.unet_in_channels:
            raise ValueError(
                f"Incorrect configuration settings! The config of `pipeline.unet`: expects"
                f" {self.unet_in_channels} but received `num_channels_latents`: {num_channels_latents} +"
                f" `num_channels_image`: {num_channels_image} "
                f" = {num_channels_latents+num_channels_image}. Please verify the config of"
                " `pipeline.unet` or your `image` input."
            )
            
        timestep_dtype = next(
            (input.type for input in self.unet.model.get_inputs() if input.name == "timestep"), "tensor(float)"
        )
        timestep_dtype = ORT_TO_NP_TYPE[timestep_dtype]

        # 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, torch_gen)

        # 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.
                # The latents are expanded 3 times because for pix2pix the guidance\
                # is applied for both the text and the input image.
                latent_model_input = np.concatenate([latents] * 3) if do_classifier_free_guidance else latents
                
                scaled_latent_model_input = self.scheduler.scale_model_input(torch.from_numpy(latent_model_input), t)
                scaled_latent_model_input = scaled_latent_model_input.cpu().numpy()

                scaled_latent_model_input = np.concatenate([scaled_latent_model_input, image_latents], axis=1)

                # predict the noise residual
                             
                noise_pred = self.unet(
                    sample=scaled_latent_model_input,
                    timestep=np.array([t], dtype=timestep_dtype),
                    encoder_hidden_states=prompt_embeds,
                )[0]
                
                # Hack:
                # For karras style schedulers the model does classifer free guidance using the
                # predicted_original_sample instead of the noise_pred. So we need to compute the
                # predicted_original_sample here if we are using a karras style scheduler.
                if scheduler_is_in_sigma_space:
                    step_index = (self.scheduler.timesteps == t).nonzero().item()
                    sigma = self.scheduler.sigmas[step_index]
                    noise_pred = latent_model_input - sigma.numpy() * noise_pred

                # perform guidance
                if do_classifier_free_guidance:
                    noise_pred_text, noise_pred_image, noise_pred_uncond = np.split(noise_pred, 3)
                    noise_pred = (
                        noise_pred_uncond
                        + guidance_scale * (noise_pred_text - noise_pred_image)
                        + image_guidance_scale * (noise_pred_image - noise_pred_uncond)
                    )

                # Hack:
                # For karras style schedulers the model does classifer free guidance using the
                # predicted_original_sample instead of the noise_pred. But the scheduler.step function
                # expects the noise_pred and computes the predicted_original_sample internally. So we
                # need to overwrite the noise_pred here such that the value of the computed
                # predicted_original_sample is correct.
                if scheduler_is_in_sigma_space:
                    noise_pred = (noise_pred - latents) / (-sigma)

                # compute the previous noisy sample x_t -> x_t-1
                scheduler_output = self.scheduler.step(
                    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):
                    progress_bar.update()
                    if callback is not None and i % callback_steps == 0:
                        callback(i, t, latents.numpy())

        # 10. Post-processing
        image = self.decode_latents(latents)

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

        # 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)

    def _encode_prompt(self, prompt, num_images_per_prompt, do_classifier_free_guidance, negative_prompt):
        r"""
        Encodes the prompt into text encoder hidden states.

        Args:
            prompt (`str` or `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 (`str` or `List[str]`):
                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`).
        """
        negative_prompt_embeds = None
        batch_size = len(prompt) if isinstance(prompt, list) else 1

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

        if not np.array_equal(text_input_ids, untruncated_ids):
            removed_text = self.tokenizer.batch_decode(untruncated_ids[:, self.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" {self.tokenizer.model_max_length} tokens: {removed_text}"
            )

        prompt_embeds = self.text_encoder(input_ids=text_input_ids.astype(np.int32))[0]  
        prompt_embeds = np.repeat(prompt_embeds, num_images_per_prompt, axis=0)

        # get unconditional embeddings for classifier free guidance
        if do_classifier_free_guidance:
            uncond_tokens: List[str]
            if negative_prompt is None:
                uncond_tokens = [""] * batch_size
            elif 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] * batch_size
            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

            max_length = text_input_ids.shape[-1]
            uncond_input = self.tokenizer(
                uncond_tokens,
                padding="max_length",
                max_length=max_length,
                truncation=True,
                return_tensors="np",
            )
            negative_prompt_embeds = self.text_encoder(input_ids=uncond_input.input_ids.astype(np.int32))[0]
            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
            # pix2pix has two  negative embeddings, and unlike in other pipelines latents are ordered [prompt_embeds, negative_prompt_embeds, negative_prompt_embeds]

            prompt_embeds = np.concatenate((prompt_embeds, negative_prompt_embeds, negative_prompt_embeds))

        return prompt_embeds

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker
    def run_safety_checker(self, image):
        if self.safety_checker is not None:
            safety_checker_input = self.feature_extractor(
                self.numpy_to_pil(image), return_tensors="np"
            ).pixel_values.astype(image.dtype)
            # safety_checker does not support batched inputs yet
            images, has_nsfw_concept = [], []
            for i in range(image.shape[0]):
                image_i, has_nsfw_concept_i = self.safety_checker(
                    clip_input=safety_checker_input[i : i + 1], images=image[i : i + 1]
                )
                images.append(image_i)
                has_nsfw_concept.append(has_nsfw_concept_i[0])
            image = np.concatenate(images)
        else:
            has_nsfw_concept = None
        return image, has_nsfw_concept

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs
    def prepare_extra_step_kwargs(self, generator, eta, torch_gen):
        # 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

        # check if the scheduler accepts generator
        accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys())
        if accepts_generator:
            extra_step_kwargs["generator"] = torch_gen
        return extra_step_kwargs

    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.decode_latents
    def decode_latents(self, latents):
        latents = 1 / 0.18215 * latents
        image = np.concatenate(
            [self.vae_decoder(latent_sample=latents[i : i + 1])[0] for i in range(latents.shape[0])]
        )
        image = np.clip(image / 2 + 0.5, 0, 1)
        image = image.transpose((0, 2, 3, 1))
        return image

    def check_inputs(self, prompt, callback_steps):
        if 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 (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)}."
            )