Upload stable_diff_comp_2.py

stable_diff_comp_2.py

@@ -0,0 +1,400 @@
+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("_")}
+
+    @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]])