291 lines
11 KiB
Python
291 lines
11 KiB
Python
import gc
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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
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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.utils import torch_gc, get_scheduler
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from lama_cleaner.schema import Config
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class CPUTextEncoderWrapper(torch.nn.Module):
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def __init__(self, text_encoder, torch_dtype):
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super().__init__()
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self.config = text_encoder.config
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self.text_encoder = text_encoder.to(torch.device("cpu"), non_blocking=True)
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self.text_encoder = self.text_encoder.to(torch.float32, non_blocking=True)
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self.torch_dtype = torch_dtype
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del text_encoder
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torch_gc()
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def __call__(self, x, **kwargs):
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input_device = x.device
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return [
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self.text_encoder(x.to(self.text_encoder.device), **kwargs)[0]
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.to(input_device)
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.to(self.torch_dtype)
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]
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@property
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def dtype(self):
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return self.torch_dtype
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NAMES_MAP = {
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"sd1.5": "runwayml/stable-diffusion-inpainting",
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"anything4": "Sanster/anything-4.0-inpainting",
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"realisticVision1.4": "Sanster/Realistic_Vision_V1.4-inpainting",
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}
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NATIVE_NAMES_MAP = {
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"sd1.5": "runwayml/stable-diffusion-v1-5",
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"anything4": "andite/anything-v4.0",
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"realisticVision1.4": "SG161222/Realistic_Vision_V1.4",
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}
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def make_inpaint_condition(image, image_mask):
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"""
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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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"""
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image = image.astype(np.float32) / 255.0
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image[image_mask[:, :, -1] > 128] = -1.0 # set as masked pixel
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image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
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image = torch.from_numpy(image)
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return image
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def load_from_local_model(
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local_model_path, torch_dtype, controlnet, pipe_class, is_native_control_inpaint
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):
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from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
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download_from_original_stable_diffusion_ckpt,
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)
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logger.info(f"Converting {local_model_path} to diffusers controlnet pipeline")
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try:
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pipe = download_from_original_stable_diffusion_ckpt(
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local_model_path,
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num_in_channels=4 if is_native_control_inpaint else 9,
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from_safetensors=local_model_path.endswith("safetensors"),
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device="cpu",
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load_safety_checker=False,
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)
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except Exception as e:
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err_msg = str(e)
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logger.exception(e)
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if is_native_control_inpaint and "[320, 9, 3, 3]" in err_msg:
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logger.error(
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"control_v11p_sd15_inpaint method requires normal SD model, not inpainting SD model"
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)
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if not is_native_control_inpaint and "[320, 4, 3, 3]" in err_msg:
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logger.error(
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f"{controlnet.config['_name_or_path']} method requires inpainting SD model, "
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f"you can convert any SD model to inpainting model in AUTO1111: \n"
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f"https://www.reddit.com/r/StableDiffusion/comments/zyi24j/how_to_turn_any_model_into_an_inpainting_model/"
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)
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exit(-1)
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inpaint_pipe = pipe_class(
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vae=pipe.vae,
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text_encoder=pipe.text_encoder,
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tokenizer=pipe.tokenizer,
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unet=pipe.unet,
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controlnet=controlnet,
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scheduler=pipe.scheduler,
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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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del pipe
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gc.collect()
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return inpaint_pipe.to(torch_dtype=torch_dtype)
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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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def init_model(self, device: torch.device, **kwargs):
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fp16 = not kwargs.get("no_half", False)
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model_kwargs = {
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"local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"])
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}
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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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sd_controlnet_method = kwargs["sd_controlnet_method"]
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self.sd_controlnet_method = sd_controlnet_method
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if sd_controlnet_method == "control_v11p_sd15_inpaint":
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from diffusers import StableDiffusionControlNetPipeline as PipeClass
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self.is_native_control_inpaint = True
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else:
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from .pipeline import StableDiffusionControlNetInpaintPipeline as PipeClass
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self.is_native_control_inpaint = False
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if self.is_native_control_inpaint:
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model_id = NATIVE_NAMES_MAP[kwargs["name"]]
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else:
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model_id = NAMES_MAP[kwargs["name"]]
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controlnet = ControlNetModel.from_pretrained(
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f"lllyasviel/{sd_controlnet_method}", torch_dtype=torch_dtype
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)
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self.is_local_sd_model = False
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if kwargs.get("sd_local_model_path", None):
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self.is_local_sd_model = True
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self.model = load_from_local_model(
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kwargs["sd_local_model_path"],
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torch_dtype=torch_dtype,
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controlnet=controlnet,
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pipe_class=PipeClass,
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is_native_control_inpaint=self.is_native_control_inpaint,
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)
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else:
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self.model = PipeClass.from_pretrained(
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model_id,
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controlnet=controlnet,
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revision="fp16" if use_gpu and fp16 else "main",
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torch_dtype=torch_dtype,
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**model_kwargs,
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)
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# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
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self.model.enable_attention_slicing()
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# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
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if kwargs.get("enable_xformers", False):
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self.model.enable_xformers_memory_efficient_attention()
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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 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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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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img_h, img_w = image.shape[:2]
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if self.is_native_control_inpaint:
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control_image = make_inpaint_condition(image, mask)
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output = self.model(
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prompt=config.prompt,
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image=control_image,
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height=img_h,
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width=img_w,
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num_inference_steps=config.sd_steps,
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guidance_scale=config.sd_guidance_scale,
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controlnet_conditioning_scale=config.controlnet_conditioning_scale,
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negative_prompt=config.negative_prompt,
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generator=torch.manual_seed(config.sd_seed),
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output_type="np",
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callback=self.callback,
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).images[0]
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else:
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if "canny" in self.sd_controlnet_method:
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canny_image = cv2.Canny(image, 100, 200)
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canny_image = canny_image[:, :, None]
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canny_image = np.concatenate(
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[canny_image, canny_image, canny_image], axis=2
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)
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canny_image = PIL.Image.fromarray(canny_image)
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control_image = canny_image
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elif "openpose" in self.sd_controlnet_method:
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from controlnet_aux import OpenposeDetector
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processor = OpenposeDetector.from_pretrained("lllyasviel/ControlNet")
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control_image = processor(image, hand_and_face=True)
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elif "depth" in self.sd_controlnet_method:
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from transformers import pipeline
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depth_estimator = pipeline("depth-estimation")
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depth_image = depth_estimator(PIL.Image.fromarray(image))["depth"]
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depth_image = np.array(depth_image)
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depth_image = depth_image[:, :, None]
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depth_image = np.concatenate(
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[depth_image, depth_image, depth_image], axis=2
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)
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control_image = PIL.Image.fromarray(depth_image)
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else:
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raise NotImplementedError(
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f"{self.sd_controlnet_method} not implemented"
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)
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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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control_image=control_image,
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prompt=config.prompt,
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negative_prompt=config.negative_prompt,
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mask_image=mask_image,
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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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def forward_post_process(self, result, image, mask, config):
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if config.sd_match_histograms:
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result = self._match_histograms(result, image[:, :, ::-1], mask)
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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)
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return result, image, mask
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@staticmethod
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def is_downloaded() -> bool:
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# model will be downloaded when app start, and can't switch in frontend settings
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return True
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