diffmodels/simple_diffusion.py

import diffusers
import transformers
import utils.log
import torch
import PIL
from typing import Union, Dict, Any, Optional, List, Tuple, Callable
import os
import re

class SimpleDiffusion(diffusers.DiffusionPipeline):
    """
    An unified interface for diffusion models. This allow us to use :
        - txt2img
        - img2img
        - inpainting
        - unconditional image generation
    
    This class is highly inspired from the Stable-Diffusion-Mega pipeline.
    DiffusionPipeline class allow us to load/download all the models hubbed by HuggingFace with an ease. Read more information
    about the DiffusionPipeline class here: https://huggingface.co./transformers/main_classes/pipelines.html#transformers.DiffusionPipeline
    
 Args:
        logger (:obj:`utils.log.Logger`):
            The logger to use for logging any information.
        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 ([`StableDiffusionMegaSafetyChecker`]):
            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`.

    """
    def __init__(
        self,
        vae: diffusers.AutoencoderKL,
        text_encoder: transformers.CLIPTextModel,
        tokenizer: transformers.CLIPTokenizer,
        unet: diffusers.UNet2DConditionModel,
        scheduler: Union[diffusers.DDIMScheduler, diffusers.PNDMScheduler, diffusers.LMSDiscreteScheduler],
        safety_checker: diffusers.pipelines.stable_diffusion.safety_checker.StableDiffusionSafetyChecker,
        feature_extractor: transformers.CLIPFeatureExtractor,
        prompt_generation = "succinctly/text2image-prompt-generator"
    ):
        super().__init__()
        self._logger = None
        self.register_modules( # already defined in ConfigMixin class, from_pretrained loads these modules
            vae=vae,
            text_encoder=text_encoder,
            tokenizer=tokenizer,
            unet=unet,
            scheduler=scheduler,
            safety_checker=safety_checker,
            feature_extractor=feature_extractor,
            
        ) 
        self._generated_prompts = []
        self._enable_prompt_generation = False
        if prompt_generation:
            self._enable_prompt_generation = True
            self._prompt_generator = transformers.pipeline('text-generation', model='Gustavosta/MagicPrompt-Stable-Diffusion', tokenizer='gpt2')
        
    def _generate_prompt(self, prompt, **kwargs):
        """
        Generate a prompt from a given text.
        Args:
            prompt (str): The text to generate a prompt from.
            **kwargs: Additional keyword arguments passed to the prompt generator pipeline.
        """
        max_length = kwargs.pop("max_length", None)
        num_return_sequences = kwargs.pop("num_return_sequences", None)

        prompt = self._prompt_generator(prompt, max_length=max_length, num_return_sequences=num_return_sequences)
        prompt = self._process_prompt(prompt, **kwargs)
        return prompt[0]['generated_text']

    def _process_prompt(self,original_prompt, prompt_list):
        # TODO : Add documentation; add more prompt processing
        response_list = []
        for x in prompt_list:
            resp = x['generated_text'].strip()
            if resp != original_prompt and len(resp) > (len(original_prompt) + 4) and resp.endswith((":", "-", "—")) is False:
                response_list.append(resp+'\n')

        response_end = "\n".join(response_list)
        response_end = re.sub('[^ ]+\.[^ ]+','', response_end)
        response_end = response_end.replace("<", "").replace(">", "")

        if response_end != "":
            return response_end

    # Following components are required for the DiffusionPipeline class - but they exist in the StableDiffusionModel class
    def enable_attention_slicing(self, slice_size: Optional[Union[str, int]] = "auto"):
        r"""
        Enable sliced attention computation.
        Refer to the [StableDiffusionModel](https://github.com/huggingface/diffusers/blob/main/examples/community/stable_diffusion_mega.py) repo
        for more information.
        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
            if self._logger is not None:
              self._logger.info("Attention slicing enabled!")
            slice_size = self.unet.config.attention_head_dim // 2
        self.unet.set_attention_slice(slice_size)

    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.
        """
        if self._logger is not None:
            self._logger.info("Attention slicing disabled!")
        self.enable_attention_slicing(None)

    def set_logger(self, logger):
        r"""
        Set logger. This is useful to log information about the model.
        """
        self._logger = logger 

    @property
    def components(self) -> Dict[str, Any]:
        # Return the non-private variables
        return {k : getattr(self, k) for k in self.config.keys() if not k.startswith("_")} 
    
    @torch.no_grad()
    def inpaint(
        self,
        prompt: Union[str, List[str]],
        init_image: Union[torch.FloatTensor, PIL.Image.Image],
        mask_image: Union[torch.FloatTensor, PIL.Image.Image],
        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[torch.Generator] = 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,
    ):
        if self._enable_prompt_generation:
            prompt = self._generate_prompt(p, **kwargs)[0]
            self._logger.info(f"Generated prompt: {prompt}")
        # For more information on how this function works, please see: https://huggingface.co./docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
        return diffusers.StableDiffusionInpaintPipelineLegacy(**self.components)(
            prompt=prompt,
            init_image=init_image,
            mask_image=mask_image,
            strength=strength,
            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,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
        )

    @torch.no_grad()
    def img2img(
        self,
        prompt: Union[str, List[str]],
        init_image: Union[torch.FloatTensor, PIL.Image.Image],
        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[torch.Generator] = 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,
    ):
        if self._enable_prompt_generation:
            prompt = self._generate_prompt(p, **kwargs)[0]
            self._logger.info(f"Generated prompt: {prompt}")
        # For more information on how this function works, please see: https://huggingface.co./docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionImg2ImgPipeline
        return diffusers.StableDiffusionImg2ImgPipeline(**self.components)(
            prompt=prompt,
            init_image=init_image,
            strength=strength,
            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,
            output_type=output_type,
            return_dict=return_dict,
            callback=callback,
            callback_steps=callback_steps,
        )

    @torch.no_grad()
    def text2img(
        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,
    ):
        if self._enable_prompt_generation:
            prompt = self._generate_prompt(p, **kwargs)[0]
            self._logger.info(f"Generated prompt: {prompt}")

        # For more information on how this function https://huggingface.co./docs/diffusers/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline
        return diffusers.StableDiffusionPipeline(**self.components)(
            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,
        )

    @torch.no_grad()
    def upscale(
        self,
        prompt: Union[str, List[str]],
        init_image: Union[torch.FloatTensor, PIL.Image.Image],
        num_inference_steps: Optional[int] = 75,
        guidance_scale: Optional[float] = 9.0,
        negative_prompt: Optional[Union[str, List[str]]] = None,
        num_images_per_prompt: Optional[int] = 1,
        eta: Optional[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,
    ):
        """
        Upscale an image using the StableDiffusionUpscalePipeline.
        """
        if self._enable_prompt_generation:
            prompt = self._generate_prompt(p, **kwargs)[0]
            self._logger.info(f"Generated prompt: {prompt}")

        return diffusers.StableDiffusionUpscalePipeline(**self.components)(
            prompt=prompt,
            image=init_image,
            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)

    def set_scheduler(self, scheduler: Union[diffusers.DDIMScheduler, diffusers.PNDMScheduler, diffusers.LMSDiscreteScheduler, diffusers.EulerDiscreteScheduler]):
        """
        Set the scheduler for the pipeline. This is useful for controlling the diffusion process.
        Args:
            scheduler (Union[diffusers.DDIMScheduler, diffusers.PNDMScheduler, diffusers.LMSDiscreteScheduler]): The scheduler to use.

        """
        self.components["scheduler"] = scheduler