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| 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 = "stable-diffusion-v1-5/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 | |
| 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]: | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| def process_scribble_interactive( | |
| self, | |
| image_and_mask: dict[str, np.ndarray | list[np.ndarray]] | None, | |
| 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_and_mask is None: | |
| raise ValueError | |
| if image_resolution > MAX_IMAGE_RESOLUTION: | |
| raise ValueError | |
| if num_images > MAX_NUM_IMAGES: | |
| raise ValueError | |
| image = 255 - image_and_mask["composite"] # type: ignore | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |
| 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 is None: | |
| raise ValueError | |
| 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 | |