167 lines
6.2 KiB
Python
167 lines
6.2 KiB
Python
import PIL.Image
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import cv2
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import numpy as np
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import torch
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from diffusers import ControlNetModel, DiffusionPipeline
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from loguru import logger
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from lama_cleaner.model.base import DiffusionInpaintModel
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from lama_cleaner.model.helper.controlnet_preprocess import (
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make_canny_control_image,
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make_openpose_control_image,
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make_depth_control_image,
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make_inpaint_control_image,
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)
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from lama_cleaner.model.helper.cpu_text_encoder import CPUTextEncoderWrapper
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from lama_cleaner.model.utils import get_scheduler, handle_from_pretrained_exceptions
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from lama_cleaner.schema import Config, ModelType
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class ControlNet(DiffusionInpaintModel):
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name = "controlnet"
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pad_mod = 8
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min_size = 512
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@property
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def lcm_lora_id(self):
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if self.model_info.model_type in [
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ModelType.DIFFUSERS_SD,
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ModelType.DIFFUSERS_SD_INPAINT,
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]:
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return "latent-consistency/lcm-lora-sdv1-5"
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if self.model_info.model_type in [
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ModelType.DIFFUSERS_SDXL,
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ModelType.DIFFUSERS_SDXL_INPAINT,
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]:
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return "latent-consistency/lcm-lora-sdxl"
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raise NotImplementedError(f"Unsupported controlnet lcm model {self.model_info}")
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def init_model(self, device: torch.device, **kwargs):
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fp16 = not kwargs.get("no_half", False)
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model_info = kwargs["model_info"]
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controlnet_method = kwargs["controlnet_method"]
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self.model_info = model_info
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self.controlnet_method = controlnet_method
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model_kwargs = {}
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if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False):
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logger.info("Disable Stable Diffusion Model NSFW checker")
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model_kwargs.update(
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dict(
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safety_checker=None,
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feature_extractor=None,
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requires_safety_checker=False,
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)
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)
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use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
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torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
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self.torch_dtype = torch_dtype
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if model_info.model_type in [
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ModelType.DIFFUSERS_SD,
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ModelType.DIFFUSERS_SD_INPAINT,
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]:
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from diffusers import (
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StableDiffusionControlNetInpaintPipeline as PipeClass,
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)
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elif model_info.model_type in [
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ModelType.DIFFUSERS_SDXL,
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ModelType.DIFFUSERS_SDXL_INPAINT,
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]:
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from diffusers import (
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StableDiffusionXLControlNetInpaintPipeline as PipeClass,
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)
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controlnet = ControlNetModel.from_pretrained(
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pretrained_model_name_or_path=controlnet_method,
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resume_download=True,
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)
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if model_info.is_single_file_diffusers:
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if self.model_info.model_type == ModelType.DIFFUSERS_SD:
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model_kwargs["num_in_channels"] = 4
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else:
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model_kwargs["num_in_channels"] = 9
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self.model = PipeClass.from_single_file(
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model_info.path, controlnet=controlnet, **model_kwargs
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).to(torch_dtype)
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else:
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self.model = handle_from_pretrained_exceptions(
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PipeClass.from_pretrained,
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pretrained_model_name_or_path=model_info.path,
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controlnet=controlnet,
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variant="fp16",
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dtype=torch_dtype,
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**model_kwargs,
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)
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if kwargs.get("cpu_offload", False) and use_gpu:
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logger.info("Enable sequential cpu offload")
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self.model.enable_sequential_cpu_offload(gpu_id=0)
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else:
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self.model = self.model.to(device)
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if kwargs["sd_cpu_textencoder"]:
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logger.info("Run Stable Diffusion TextEncoder on CPU")
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self.model.text_encoder = CPUTextEncoderWrapper(
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self.model.text_encoder, torch_dtype
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)
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self.callback = kwargs.pop("callback", None)
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def switch_controlnet_method(self, new_method: str):
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self.controlnet_method = new_method
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controlnet = ControlNetModel.from_pretrained(
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new_method, torch_dtype=self.torch_dtype, resume_download=True
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).to(self.model.device)
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self.model.controlnet = controlnet
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def _get_control_image(self, image, mask):
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if "canny" in self.controlnet_method:
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control_image = make_canny_control_image(image)
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elif "openpose" in self.controlnet_method:
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control_image = make_openpose_control_image(image)
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elif "depth" in self.controlnet_method:
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control_image = make_depth_control_image(image)
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elif "inpaint" in self.controlnet_method:
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control_image = make_inpaint_control_image(image, mask)
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else:
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raise NotImplementedError(f"{self.controlnet_method} not implemented")
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return control_image
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def forward(self, image, mask, config: Config):
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"""Input image and output image have same size
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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return: BGR IMAGE
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"""
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scheduler_config = self.model.scheduler.config
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scheduler = get_scheduler(config.sd_sampler, scheduler_config)
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self.model.scheduler = scheduler
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img_h, img_w = image.shape[:2]
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control_image = self._get_control_image(image, mask)
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mask_image = PIL.Image.fromarray(mask[:, :, -1], mode="L")
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image = PIL.Image.fromarray(image)
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output = self.model(
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image=image,
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mask_image=mask_image,
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control_image=control_image,
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prompt=config.prompt,
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negative_prompt=config.negative_prompt,
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num_inference_steps=config.sd_steps,
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guidance_scale=config.sd_guidance_scale,
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output_type="np",
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callback=self.callback,
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height=img_h,
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width=img_w,
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generator=torch.manual_seed(config.sd_seed),
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controlnet_conditioning_scale=config.controlnet_conditioning_scale,
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).images[0]
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output = (output * 255).round().astype("uint8")
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output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
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return output
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