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| import PIL | |
| from PIL import Image | |
| from PIL import ImageDraw | |
| import numpy as np | |
| import dlib | |
| import cv2 | |
| import torch | |
| import diffusers | |
| from diffusers import StableDiffusionPipeline, DiffusionPipeline | |
| from diffusers import ControlNetModel, StableDiffusionControlNetPipeline, StableDiffusionControlNetImg2ImgPipeline, StableDiffusionImg2ImgPipeline | |
| from chat_anything.face_generator.pipelines.lpw_stable_diffusion import StableDiffusionLongPromptWeightingPipeline, get_weighted_text_embeddings | |
| from diffusers.schedulers import EulerAncestralDiscreteScheduler,DPMSolverMultistepScheduler # DPM++ SDE Karras | |
| from chat_anything.face_generator.utils.generate import generate | |
| class LongPromptGenerator(): | |
| prompt_template = "A portrait of a {}, fine face, nice looking" | |
| negative_prompt = "easynegative,Low resolution,Low quality, Opened Mouth" | |
| # negative_prompt = "(((sexy))),paintings,loli,,big head,sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, age spot, glans, nsfw, nipples,extra fingers, ((extra arms)), (extra legs), mutated hands, (fused fingers), (too many fingers), (long neck:1.3)" | |
| def __init__(self, model_dir, lora_path=None, prompt_template="{}", negative_prompt=""): | |
| self.model_dir = model_dir | |
| self.lora_path = lora_path | |
| self.prompt_template = prompt_template | |
| self.negative_prompt = negative_prompt | |
| def load_model(self, *args, **kwargs): | |
| # load model | |
| try: | |
| pipe = DiffusionPipeline.from_pretrained(self.model_dir, torch_dtype=torch.float16, **kwargs) | |
| except: | |
| pipe = StableDiffusionPipeline.from_pretrained(self.model_dir, torch_dtype=torch.float16, **kwargs) | |
| pipe = pipe.to('cuda') | |
| sche_conf = dict(pipe.scheduler.config) | |
| fk_kwargs = ["skip_prk_steps","steps_offset","clip_sample","clip_sample_range","rescale_betas_zero_snr","timestep_spacing", "set_alpha_to_one"] | |
| for k in fk_kwargs: | |
| if k in sche_conf: | |
| sche_conf.pop(k) | |
| scheduler = DPMSolverMultistepScheduler(**sche_conf) | |
| pipe.scheduler=scheduler | |
| pipe_longprompt = StableDiffusionLongPromptWeightingPipeline(**pipe.components) | |
| self.pipe, self.pipe_longprompt = pipe, pipe_longprompt | |
| if self.lora_path is not None: | |
| pipe.load_lora_weights(self.lora_path) | |
| self.pipe_img2img = StableDiffusionImg2ImgPipeline.from_pretrained(self.model_dir, **pipe.components) | |
| def generate( | |
| self, | |
| prompt, | |
| do_inversion=False, | |
| **kwargs, | |
| ): | |
| """ | |
| Face control generating. | |
| """ | |
| print('GENERATING:', prompt) | |
| if not do_inversion: | |
| generating_conf = { | |
| "pipe": self.pipe, | |
| "prompt": prompt, | |
| "negative_prompt": self.negative_prompt, | |
| "num_inference_steps": 25, | |
| "guidance_scale": 7, | |
| } | |
| else: | |
| assert 'image' in kwargs, 'doing inversion, prepare the init image please PIL Image' | |
| init_image = kwargs['image'] | |
| generating_conf = { | |
| "pipe": self.pipe_img2img, | |
| "prompt": prompt, | |
| "negative_prompt": self.negative_prompt, | |
| "image": init_image, | |
| "num_inference_steps": 25, | |
| "guidance_scale": 7, | |
| "strength": kwargs.pop('strength', 0.9), | |
| } | |
| pipe_out = generate(**generating_conf) | |
| generated_img = pipe_out[0][0] | |
| return generated_img |