modules/onnx_impl/pipelines/onnx_stable_diffusion_img2img_pipeline.py

import inspect
from typing import Union, Optional, Callable, List, Any
import numpy as np
import torch
import diffusers
from diffusers.pipelines.onnx_utils import ORT_TO_NP_TYPE
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.image_processor import VaeImageProcessor, PipelineImageInput
from modules.onnx_impl.pipelines import CallablePipelineBase
from modules.onnx_impl.pipelines.utils import randn_tensor


class OnnxStableDiffusionImg2ImgPipeline(diffusers.OnnxStableDiffusionImg2ImgPipeline, CallablePipelineBase):
    __module__ = 'diffusers'
    __name__ = 'OnnxStableDiffusionImg2ImgPipeline'

    image_processor: VaeImageProcessor

    def __init__(
        self,
        vae_encoder: diffusers.OnnxRuntimeModel,
        vae_decoder: diffusers.OnnxRuntimeModel,
        text_encoder: diffusers.OnnxRuntimeModel,
        tokenizer: Any,
        unet: diffusers.OnnxRuntimeModel,
        scheduler: Any,
        safety_checker: diffusers.OnnxRuntimeModel,
        feature_extractor: Any,
        requires_safety_checker: bool = True
    ):
        super().__init__(vae_encoder, vae_decoder, text_encoder, tokenizer, unet, scheduler, safety_checker, feature_extractor, requires_safety_checker)
        self.image_processor = VaeImageProcessor(vae_scale_factor=64)

    def __call__(
        self,
        prompt: Union[str, List[str]],
        image: PipelineImageInput = None,
        strength: float = 0.8,
        num_inference_steps: Optional[int] = 50,
        guidance_scale: Optional[float] = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: Optional[float] = 0.0,
        generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None,
        prompt_embeds: Optional[np.ndarray] = None,
        negative_prompt_embeds: 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,
    ):
        # check inputs. Raise error if not correct
        self.check_inputs(prompt, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds)

        # define call parameters
        if prompt is not None and isinstance(prompt, str):
            batch_size = 1
        elif prompt is not None and isinstance(prompt, list):
            batch_size = len(prompt)
        else:
            batch_size = prompt_embeds.shape[0]

        if generator is None:
            generator = torch.Generator("cpu")

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

        # set timesteps
        self.scheduler.set_timesteps(num_inference_steps)

        image = self.image_processor.preprocess(image).cpu().numpy()

        # 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

        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,
        )

        scaling_factor = self.vae_decoder.config.get("scaling_factor", 0.18215)

        latents_dtype = prompt_embeds.dtype
        image = image.astype(latents_dtype)
        # encode the init image into latents and scale the latents
        init_latents = self.vae_encoder(sample=image)[0]
        init_latents = scaling_factor * init_latents

        if isinstance(prompt, str):
            prompt = [prompt]

        init_latents = np.concatenate([init_latents] * num_images_per_prompt, axis=0)

        # get the original timestep using init_timestep
        offset = self.scheduler.config.get("steps_offset", 0)
        init_timestep = int(num_inference_steps * strength) + offset
        init_timestep = min(init_timestep, num_inference_steps)

        timesteps = self.scheduler.timesteps.numpy()[-init_timestep]
        timesteps = np.array([timesteps] * batch_size * num_images_per_prompt)

        # add noise to latents using the timesteps
        noise = randn_tensor(init_latents.shape, latents_dtype, generator)
        init_latents = self.scheduler.add_noise(
            torch.from_numpy(init_latents), torch.from_numpy(noise), torch.from_numpy(timesteps)
        )
        init_latents = init_latents.numpy()

        # 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)
        timesteps = self.scheduler.timesteps[t_start:].numpy()

        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]

        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)[
                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)

            # 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 callback is not None and i % callback_steps == 0:
                step_idx = i // getattr(self.scheduler, "order", 1)
                callback(step_idx, t, latents)

        has_nsfw_concept = None

        if output_type != "latent":
            latents /= scaling_factor

            # image = self.vae_decoder(latent_sample=latents)[0]
            # 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])]
            )

            image = np.clip(image / 2 + 0.5, 0, 1)
            image = image.transpose((0, 2, 3, 1))

            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)

                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)

            if output_type == "pil":
                image = self.numpy_to_pil(image)
        else:
            image = latents

        # skip postprocess

        if not return_dict:
            return (image, has_nsfw_concept)

        return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept)