Delete pipeline.py

pipeline.py

@@ -1,365 +0,0 @@
-from typing import Any, Callable, Dict, List, Optional, Union
-
-import torch
-
-from diffusers import DiffusionPipeline, StableDiffusionPipeline
-from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
-
-
-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],
-    ):
-        super()._init_()
-
-        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
-
-        self.register_modules(pipeline1=self.pipe1, pipeline2=self.pipe2, pipeline3=self.pipe3, pipeline4=self.pipe4)
-
-    @property
-    def layers(self) -> Dict[str, Any]:
-        return {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
-
-    @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]])