model.py

from __future__ import annotations

import gc

import numpy as np
import PIL.Image
import torch
from controlnet_aux.util import HWC3
from diffusers import (ControlNetModel, DiffusionPipeline,
                       StableDiffusionControlNetPipeline,
                       UniPCMultistepScheduler)

from cv_utils import resize_image
from preprocessor import Preprocessor
from settings import MAX_IMAGE_RESOLUTION, MAX_NUM_IMAGES

CONTROLNET_MODEL_IDS = {
    'Openpose': 'lllyasviel/control_v11p_sd15_openpose',
    'Canny': 'lllyasviel/control_v11p_sd15_canny',
    'MLSD': 'lllyasviel/control_v11p_sd15_mlsd',
    'scribble': 'lllyasviel/control_v11p_sd15_scribble',
    'softedge': 'lllyasviel/control_v11p_sd15_softedge',
    'segmentation': 'lllyasviel/control_v11p_sd15_seg',
    'depth': 'lllyasviel/control_v11f1p_sd15_depth',
    'NormalBae': 'lllyasviel/control_v11p_sd15_normalbae',
    'lineart': 'lllyasviel/control_v11p_sd15_lineart',
    'lineart_anime': 'lllyasviel/control_v11p_sd15s2_lineart_anime',
    'shuffle': 'lllyasviel/control_v11e_sd15_shuffle',
    'ip2p': 'lllyasviel/control_v11e_sd15_ip2p',
    'inpaint': 'lllyasviel/control_v11e_sd15_inpaint',
}


def download_all_controlnet_weights() -> None:
    for model_id in CONTROLNET_MODEL_IDS.values():
        ControlNetModel.from_pretrained(model_id)


class Model:
    def __init__(self,
                 base_model_id: str = 'runwayml/stable-diffusion-v1-5',
                 task_name: str = 'Canny'):
        self.device = torch.device(
            'cuda:0' if torch.cuda.is_available() else 'cpu')
        self.base_model_id = ''
        self.task_name = ''
        self.pipe = self.load_pipe(base_model_id, task_name)
        self.preprocessor = Preprocessor()

    def load_pipe(self, base_model_id: str, task_name) -> DiffusionPipeline:
        if base_model_id == self.base_model_id and task_name == self.task_name and hasattr(
                self, 'pipe') and self.pipe is not None:
            return self.pipe
        model_id = CONTROLNET_MODEL_IDS[task_name]
        controlnet = ControlNetModel.from_pretrained(model_id,
                                                     torch_dtype=torch.float16)
        pipe = StableDiffusionControlNetPipeline.from_pretrained(
            base_model_id,
            safety_checker=None,
            controlnet=controlnet,
            torch_dtype=torch.float16)
        pipe.scheduler = UniPCMultistepScheduler.from_config(
            pipe.scheduler.config)
        if self.device.type == 'cuda':
            pipe.enable_xformers_memory_efficient_attention()
        pipe.to(self.device)
        torch.cuda.empty_cache()
        gc.collect()
        self.base_model_id = base_model_id
        self.task_name = task_name
        return pipe

    def set_base_model(self, base_model_id: str) -> str:
        if not base_model_id or base_model_id == self.base_model_id:
            return self.base_model_id
        del self.pipe
        torch.cuda.empty_cache()
        gc.collect()
        try:
            self.pipe = self.load_pipe(base_model_id, self.task_name)
        except Exception:
            self.pipe = self.load_pipe(self.base_model_id, self.task_name)
        return self.base_model_id

    def load_controlnet_weight(self, task_name: str) -> None:
        if task_name == self.task_name:
            return
        if self.pipe is not None and hasattr(self.pipe, 'controlnet'):
            del self.pipe.controlnet
        torch.cuda.empty_cache()
        gc.collect()
        model_id = CONTROLNET_MODEL_IDS[task_name]
        controlnet = ControlNetModel.from_pretrained(model_id,
                                                     torch_dtype=torch.float16)
        controlnet.to(self.device)
        torch.cuda.empty_cache()
        gc.collect()
        self.pipe.controlnet = controlnet
        self.task_name = task_name

    def get_prompt(self, prompt: str, additional_prompt: str) -> str:
        if not prompt:
            prompt = additional_prompt
        else:
            prompt = f'{prompt}, {additional_prompt}'
        return prompt

    @torch.autocast('cuda')
    def run_pipe(
        self,
        prompt: str,
        negative_prompt: str,
        control_image: PIL.Image.Image,
        num_images: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
    ) -> list[PIL.Image.Image]:
        if seed == -1:
            seed = np.random.randint(0, np.iinfo(np.int64).max)
        generator = torch.Generator().manual_seed(seed)
        return self.pipe(prompt=prompt,
                         negative_prompt=negative_prompt,
                         guidance_scale=guidance_scale,
                         num_images_per_prompt=num_images,
                         num_inference_steps=num_steps,
                         generator=generator,
                         image=control_image).images

    @torch.inference_mode()
    def process_canny(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        low_threshold: int,
        high_threshold: int,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        self.preprocessor.load('Canny')
        control_image = self.preprocessor(image=image,
                                          low_threshold=low_threshold,
                                          high_threshold=high_threshold,
                                          detect_resolution=image_resolution)

        self.load_controlnet_weight('Canny')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_mlsd(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        preprocess_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        value_threshold: float,
        distance_threshold: float,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        self.preprocessor.load('MLSD')
        control_image = self.preprocessor(
            image=image,
            image_resolution=image_resolution,
            detect_resolution=preprocess_resolution,
            thr_v=value_threshold,
            thr_d=distance_threshold,
        )
        self.load_controlnet_weight('MLSD')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_scribble(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        preprocess_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        preprocessor_name: str,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        if preprocessor_name == 'None':
            image = HWC3(image)
            image = resize_image(image, resolution=image_resolution)
            control_image = PIL.Image.fromarray(image)
        elif preprocessor_name == 'HED':
            self.preprocessor.load(preprocessor_name)
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
                scribble=False,
            )
        elif preprocessor_name == 'PidiNet':
            self.preprocessor.load(preprocessor_name)
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
                safe=False,
            )
        self.load_controlnet_weight('scribble')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_scribble_interactive(
        self,
        image_and_mask: dict[str, np.ndarray],
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        image = image_and_mask['mask']
        image = HWC3(image)
        image = resize_image(image, resolution=image_resolution)
        control_image = PIL.Image.fromarray(image)

        self.load_controlnet_weight('scribble')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_softedge(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        preprocess_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        preprocessor_name: str,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        if preprocessor_name == 'None':
            image = HWC3(image)
            image = resize_image(image, resolution=image_resolution)
            control_image = PIL.Image.fromarray(image)
        elif preprocessor_name in ['HED', 'HED safe']:
            safe = 'safe' in preprocessor_name
            self.preprocessor.load('HED')
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
                scribble=safe,
            )
        elif preprocessor_name in ['PidiNet', 'PidiNet safe']:
            safe = 'safe' in preprocessor_name
            self.preprocessor.load('PidiNet')
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
                safe=safe,
            )
        else:
            raise ValueError
        self.load_controlnet_weight('softedge')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_openpose(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        preprocess_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        preprocessor_name: str,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        if preprocessor_name == 'None':
            image = HWC3(image)
            image = resize_image(image, resolution=image_resolution)
            control_image = PIL.Image.fromarray(image)
        else:
            self.preprocessor.load('Openpose')
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
                hand_and_face=True,
            )
        self.load_controlnet_weight('Openpose')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_segmentation(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        preprocess_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        preprocessor_name: str,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        if preprocessor_name == 'None':
            image = HWC3(image)
            image = resize_image(image, resolution=image_resolution)
            control_image = PIL.Image.fromarray(image)
        else:
            self.preprocessor.load(preprocessor_name)
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
            )
        self.load_controlnet_weight('segmentation')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_depth(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        preprocess_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        preprocessor_name: str,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        if preprocessor_name == 'None':
            image = HWC3(image)
            image = resize_image(image, resolution=image_resolution)
            control_image = PIL.Image.fromarray(image)
        else:
            self.preprocessor.load(preprocessor_name)
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
            )
        self.load_controlnet_weight('depth')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_normal(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        preprocess_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        preprocessor_name: str,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        if preprocessor_name == 'None':
            image = HWC3(image)
            image = resize_image(image, resolution=image_resolution)
            control_image = PIL.Image.fromarray(image)
        else:
            self.preprocessor.load('NormalBae')
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
            )
        self.load_controlnet_weight('NormalBae')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_lineart(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        preprocess_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        preprocessor_name: str,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        if preprocessor_name in ['None', 'None (anime)']:
            image = HWC3(image)
            image = resize_image(image, resolution=image_resolution)
            control_image = PIL.Image.fromarray(image)
        elif preprocessor_name in ['Lineart', 'Lineart coarse']:
            coarse = 'coarse' in preprocessor_name
            self.preprocessor.load('Lineart')
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
                coarse=coarse,
            )
        elif preprocessor_name == 'Lineart (anime)':
            self.preprocessor.load('LineartAnime')
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
                detect_resolution=preprocess_resolution,
            )
        if 'anime' in preprocessor_name:
            self.load_controlnet_weight('lineart_anime')
        else:
            self.load_controlnet_weight('lineart')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_shuffle(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
        preprocessor_name: str,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        if preprocessor_name == 'None':
            image = HWC3(image)
            image = resize_image(image, resolution=image_resolution)
            control_image = PIL.Image.fromarray(image)
        else:
            self.preprocessor.load(preprocessor_name)
            control_image = self.preprocessor(
                image=image,
                image_resolution=image_resolution,
            )
        self.load_controlnet_weight('shuffle')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results

    @torch.inference_mode()
    def process_ip2p(
        self,
        image: np.ndarray,
        prompt: str,
        additional_prompt: str,
        negative_prompt: str,
        num_images: int,
        image_resolution: int,
        num_steps: int,
        guidance_scale: float,
        seed: int,
    ) -> list[PIL.Image.Image]:
        if image_resolution > MAX_IMAGE_RESOLUTION:
            raise ValueError
        if num_images > MAX_NUM_IMAGES:
            raise ValueError

        image = HWC3(image)
        image = resize_image(image, resolution=image_resolution)
        control_image = PIL.Image.fromarray(image)
        self.load_controlnet_weight('ip2p')
        results = self.run_pipe(
            prompt=self.get_prompt(prompt, additional_prompt),
            negative_prompt=negative_prompt,
            control_image=control_image,
            num_images=num_images,
            num_steps=num_steps,
            guidance_scale=guidance_scale,
            seed=seed,
        )
        return [control_image] + results