pipeline.py

from typing import Any, Callable, Dict, List, Optional, Union

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

pipe1_model_id = "CompVis/stable-diffusion-v1-1"
pipe2_model_id = "CompVis/stable-diffusion-v1-2"
pipe3_model_id = "CompVis/stable-diffusion-v1-3"
pipe4_model_id = "CompVis/stable-diffusion-v1-4"


class StableDiffusionComparisonPipeline(DiffusionPipeline):
    r"""
    Pipeline for parallel comparison of Stable Diffusion v1-v4
    This pipeline inherits from DiffusionPipeline and depends on the use of an Auth Token for
    downloading pre-trained checkpoints from Hugging Face Hub.
    Args:
        pipe1 ('StableDiffusionPipeline' or 'str', optional):
            A Stable Diffusion Pipeline prepared from the SD1.1 Checkpoints on Hugging Face Hub
        pipe2 ('StableDiffusionPipeline' or 'str', optional):
            A Stable Diffusion Pipeline prepared from the SD1.2 Checkpoints on Hugging Face Hub
        pipe3 ('StableDiffusionPipeline' or 'str', optional):
            A Stable Diffusion Pipeline prepared from the SD1.3 Checkpoints on Hugging Face Hub
        pipe4 ('StableDiffusionPipeline' or 'str', optional):
            A Stable Diffusion Pipeline prepared from the SD1.4 Checkpoints on Hugging Face Hub
    """

    # def _init_(
    #     self,
    #     sd1_1: Union[StableDiffusionPipeline, str],
    #     sd1_2: Union[StableDiffusionPipeline, str],
    #     sd1_3: Union[StableDiffusionPipeline, str],
    #     sd1_4: Union[StableDiffusionPipeline, str],
    # ):
    def __init__(
        self,
        vae: AutoencoderKL,
        text_encoder: CLIPTextModel,
        tokenizer: CLIPTokenizer,
        unet: UNet2DConditionModel,
        scheduler: Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler],
        safety_checker: StableDiffusionSafetyChecker,
        feature_extractor: CLIPFeatureExtractor,
        requires_safety_checker: bool = True,
    ):
        super()._init_()

        self.pipe1 = StableDiffusionPipeline.from_pretrained(pipe1_model_id)
        self.pipe2 = StableDiffusionPipeline.from_pretrained(pipe2_model_id)
        self.pipe3 = StableDiffusionPipeline.from_pretrained(pipe3_model_id)
        self.pipe4 = StableDiffusionPipeline(
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
            requires_safety_checker=requires_safety_checker
        )

        self.register_modules(pipeline1=self.pipe1, pipeline2=self.pipe2, pipeline3=self.pipe3, pipeline4=self.pipe4)
        

        # if not isinstance(sd1_1, StableDiffusionPipeline):
        #     self.pipe1 = StableDiffusionPipeline.from_pretrained(
        #         pipe1_model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=True
        #     )
        # else:
        #     self.pipe1 = sd1_1
        # if not isinstance(sd1_2, StableDiffusionPipeline):
        #     self.pipe2 = StableDiffusionPipeline.from_pretrained(
        #         pipe2_model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=True
        #     )
        # else:
        #     self.pipe2 = sd1_2
        # if not isinstance(sd1_3, StableDiffusionPipeline):
        #     self.pipe3 = StableDiffusionPipeline.from_pretrained(
        #         pipe3_model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=True
        #     )
        # else:
        #     self.pipe3 = sd1_3
        # if not isinstance(sd1_4, StableDiffusionPipeline):
        #     self.pipe4 = StableDiffusionPipeline.from_pretrained(
        #         pipe4_model_id, torch_dtype=torch.float16, revision="fp16", use_auth_token=True
        #     )
        # else:
        #     self.pipe4 = sd1_4

    @property
    def layers(self) -> Dict[str, Any]:
        return {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}

    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_1 = self.pipe1.unet.config.attention_head_dim // 2
            slice_size_2 = self.pipe2.unet.config.attention_head_dim // 2
            slice_size_3 = self.pipe3.unet.config.attention_head_dim // 2
            slice_size_4 = self.pipe4.unet.config.attention_head_dim // 2
        self.pipe1.unet.set_attention_slice(slice_size_1)
        self.pipe2.unet.set_attention_slice(slice_size_2)
        self.pipe3.unet.set_attention_slice(slice_size_3)
        self.pipe4.unet.set_attention_slice(slice_size_4)

    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 text2img_sd1_1(
        self,
        prompt: Union[str, List[str]],
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        **kwargs,
    ):
        return self.pipe1(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
            **kwargs,
        )

    @torch.no_grad()
    def text2img_sd1_2(
        self,
        prompt: Union[str, List[str]],
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        **kwargs,
    ):
        return self.pipe2(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
            **kwargs,
        )

    @torch.no_grad()
    def text2img_sd1_3(
        self,
        prompt: Union[str, List[str]],
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        **kwargs,
    ):
        return self.pipe3(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
            **kwargs,
        )

    @torch.no_grad()
    def text2img_sd1_4(
        self,
        prompt: Union[str, List[str]],
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        **kwargs,
    ):
        return self.pipe4(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
            **kwargs,
        )

    @torch.no_grad()
    def _call_(
        self,
        prompt: Union[str, List[str]],
        height: int = 512,
        width: int = 512,
        num_inference_steps: int = 50,
        guidance_scale: float = 7.5,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: float = 0.0,
        generator: Optional[torch.Generator] = None,
        latents: Optional[torch.FloatTensor] = None,
        output_type: Optional[str] = "pil",
        return_dict: bool = True,
        callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None,
        callback_steps: Optional[int] = 1,
        **kwargs,
    ):
        r"""
        Function invoked when calling the pipeline for generation. This function will generate 4 results as part
        of running all the 4 pipelines for SD1.1-1.4 together in a serial-processing, parallel-invocation fashion.
        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`.
        """

        device = "cuda" if torch.cuda.is_available() else "cpu"
        self.to(device)

        # Checks if the height and width are divisible by 8 or not
        if height % 8 != 0 or width % 8 != 0:
            raise ValueError(f"`height` and `width` must be divisible by 8 but are {height} and {width}.")

        # Get first result from Stable Diffusion Checkpoint v1.1
        res1 = self.text2img_sd1_1(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
            **kwargs,
        )

        # Get first result from Stable Diffusion Checkpoint v1.2
        res2 = self.text2img_sd1_2(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
            **kwargs,
        )

        # Get first result from Stable Diffusion Checkpoint v1.3
        res3 = self.text2img_sd1_3(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
            **kwargs,
        )

        # Get first result from Stable Diffusion Checkpoint v1.4
        res4 = self.text2img_sd1_4(
            prompt=prompt,
            height=height,
            width=width,
            num_inference_steps=num_inference_steps,
            guidance_scale=guidance_scale,
            negative_prompt=negative_prompt,
            num_images_per_prompt=num_images_per_prompt,
            eta=eta,
            generator=generator,
            latents=latents,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
            **kwargs,
        )

        # Get all result images into a single list and pass it via StableDiffusionPipelineOutput for final result
        return StableDiffusionPipelineOutput([res1[0], res2[0], res3[0], res4[0]])